Data mining An overview from a database perspective (1996)
DataMining分析方法
如有你有帮助，请购买下载，谢谢！数据挖掘Data Mining第一部 Data Mining的觀念............... 错误！未定义书签。
第一章何謂Data Mining ..................................................... 错误！未定义书签。
第二章Data Mining運用的理論與實際應用功能............. 错误！未定义书签。
第三章Data Mining與統計分析有何不同......................... 错误！未定义书签。
第四章完整的Data Mining有哪些步驟............................ 错误！未定义书签。
第五章CRISP-DM ............................................................... 错误！未定义书签。
第六章Data Mining、Data Warehousing、OLAP三者關係為何. 错误！未定义书签。
第七章Data Mining在CRM中扮演的角色為何.............. 错误！未定义书签。
第八章Data Mining 與Web Mining有何不同................. 错误！未定义书签。
第九章Data Mining 的功能................................................ 错误！未定义书签。
第十章Data Mining應用於各領域的情形......................... 错误！未定义书签。
第十一章Data Mining的分析工具..................................... 错误！未定义书签。
第二部多變量分析....................... 错误！未定义书签。
第一章主成分分析(Principal Component Analysis) ........... 错误！未定义书签。
数据挖掘导论英文版
数据挖掘导论英文版Data Mining IntroductionData mining is the process of extracting valuable insights and patterns from large datasets. It involves the application of various techniques and algorithms to uncover hidden relationships, trends, and anomalies that can be used to inform decision-making and drive business success. In today's data-driven world, the ability to effectively harness the power of data has become a critical competitive advantage for organizations across a wide range of industries.One of the key strengths of data mining is its versatility. It can be applied to a wide range of domains, from marketing and finance to healthcare and scientific research. In the marketing realm, for example, data mining can be used to analyze customer behavior, identify target segments, and develop personalized marketing strategies. In the financial sector, data mining can be leveraged to detect fraud, assess credit risk, and optimize investment portfolios.At the heart of data mining lies a diverse set of techniques and algorithms. These include supervised learning methods, such asregression and classification, which can be used to predict outcomes based on known patterns in the data. Unsupervised learning techniques, such as clustering and association rule mining, can be employed to uncover hidden structures and relationships within datasets. Additionally, advanced algorithms like neural networks and decision trees have proven to be highly effective in tackling complex, non-linear problems.The process of data mining typically involves several key steps, each of which plays a crucial role in extracting meaningful insights from the data. The first step is data preparation, which involves cleaning, transforming, and integrating the raw data into a format that can be effectively analyzed. This step is particularly important, as the quality and accuracy of the input data can significantly impact the reliability of the final results.Once the data is prepared, the next step is to select the appropriate data mining techniques and algorithms to apply. This requires a deep understanding of the problem at hand, as well as the strengths and limitations of the available tools. Depending on the specific goals of the analysis, the data mining practitioner may choose to employ a combination of techniques, each of which can provide unique insights and perspectives.The next phase is the actual data mining process, where the selectedalgorithms are applied to the prepared data. This can involve complex mathematical and statistical calculations, as well as the use of specialized software and computing resources. The results of this process may include the identification of patterns, trends, and relationships within the data, as well as the development of predictive models and other data-driven insights.Once the data mining process is complete, the final step is to interpret and communicate the findings. This involves translating the technical results into actionable insights that can be easily understood by stakeholders, such as business leaders, policymakers, or scientific researchers. Effective communication of data mining results is crucial, as it enables decision-makers to make informed choices and take appropriate actions based on the insights gained.One of the most exciting aspects of data mining is its continuous evolution and the emergence of new techniques and technologies. As the volume and complexity of data continue to grow, the need for more sophisticated and powerful data mining tools and algorithms has become increasingly pressing. Advances in areas such as machine learning, deep learning, and big data processing have opened up new frontiers in data mining, enabling practitioners to tackle increasingly complex problems and extract even more valuable insights from the data.In conclusion, data mining is a powerful and versatile tool that has the potential to transform the way we approach a wide range of challenges and opportunities. By leveraging the power of data and the latest analytical techniques, organizations can gain a deeper understanding of their operations, customers, and markets, and make more informed, data-driven decisions that drive sustainable growth and success. As the field of data mining continues to evolve, it is clear that it will play an increasingly crucial role in shaping the future of business, science, and society as a whole.。
Data mining techniques for customer
Technology in Society 24 (2002) 483–502 /locate/techsoc
Data mining techniques for customer relationship management
Chris Rygielski a, Jyun-Cheng Wang b, David C. Yen a,∗
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Corresponding author. Tel.: +1-513-529-4826; fax: +1-513-529-9689. E-mail address: yendc@ (D.C. Yen).
0160-791X/02/$ - see front matter 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 1 6 0 - 7 9 1 X ( 0 2 ) 0 0 0 3 8 - 6
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C. Rygielski et al. / Technology in Society 24 (2002) 483–502
1. Introduction A new business culture is developing today. Within it, the economics of customer relationships are changing in fundamental ways, and companies are facing the need to implement new solutions and strategies that address these changes. The concepts of mass production and mass marketing, first created during the Industrial Revolution, are being supplanted by new ideas in which customer relationships are the central business issue. Firms today areue through analysis of the customer lifecycle. The tools and technologies of data warehousing, data mining, and other customer relationship management (CRM) techniques afford new opportunities for businesses to act on the concepts of relationship marketing. The old model of “design-build-sell” (a product-oriented view) is being replaced by “sell-build-redesign” (a customer-oriented view). The traditional process of massmarketing is being challenged by the new approach of one-to-one marketing. In the traditional process, the marketing goal is to reach more customers and expand the customer base. But given the high cost of acquiring new customers, it makes better sense to conduct business with current customers. In so doing, the marketing focus shifts away from the breadth of customer base to the depth of each customer’s needs. The performance metric changes from market share to so-called “wallet share”. Businesses do not just deal with customers in order to make transactions; they turn the opportunity to sell products into a service experience and endeavor to establish a long-term relationship with each customer. The advent of the Internet has undoubtedly contributed to the shift of marketing focus. As on-line information becomes more accessible and abundant, consumers become more informed and sophisticated. They are aware of all that is being offered, and they demand the best. To cope with this condition, businesses have to distinguish their products or services in a way that avoids the undesired result of becoming mere commodities. One effective way to distinguish themselves is with systems that can interact precisely and consistently with customers. Collecting customer demographics and behavior data makes precision targeting possible. This kind of targeting also helps when devising an effective promotion plan to meet tough competition or identifying prospective customers when new products appear. Interacting with customers consistently means businesses must store transaction records and responses in an online system that is available to knowledgeable staff members who know how to interact with it. The importance of establishing close customer relationships is recognized, and CRM is called for. It may seem that CRM is applicable only for managing relationships between businesses and consumers. A closer examination reveals that it is even more crucial for business customers. In business-to-business (B2B) environments, a tremendous amount of information is exchanged on a regular basis. For example, transactions are more numerous, custom contracts are more diverse, and pricing schemes are more complicated. CRM helps smooth the process when various representatives of seller and buyer companies communicate and collaborate. Customized catalogues, personalized business portals, and targeted product offers can simplify the procurement process and improve efficiencies for both companies. E-mail alerts and new
Introduction to Data Mining
Introduction to Data MiningData mining is a process of extracting useful information from large datasets by using various statistical and machine learning techniques. It is a crucial part of the field of data science and plays a key role in helping businesses make informed decisions based on data-driven insights.One of the main goals of data mining is to discover patterns and relationships within data that can be used to make predictions or identify trends. This can help businesses improve their marketing strategies, optimize their operations, and better understand their customers. By analyzing large amounts of data, data mining algorithms can uncover hidden patterns that may not be immediately apparent to human analysts.There are several different techniques that are commonly used in data mining, including classification, clustering, association rule mining, and anomaly detection. Classification involves categorizing data points into different classes based on their attributes, while clustering groups similar data points together. Association rule mining identifies relationships between different variables, and anomaly detection detects outliers or unusual patterns in the data.In order to apply data mining techniques effectively, it is important to have a solid understanding of statistics, machine learning, and data analytics. Data mining professionals must be able to preprocess data, select appropriate algorithms, and interpret the results of their analyses. They must also be able to communicate their findings effectively to stakeholders in order to drive business decisions.Data mining is used in a wide range of industries, including finance, healthcare, retail, and telecommunications. In finance, data mining is used to detect fraudulent transactions and predict market trends. In healthcare, it is used to analyze patient data and improve treatment outcomes. In retail, it is used to optimize inventory management and personalize marketing campaigns. In telecommunications, it is used to analyze network performance and customer behavior.Overall, data mining is a powerful tool that can help businesses gain valuable insights from their data and make more informed decisions. By leveraging the latest advances in machine learning and data analytics, organizations can stay competitive in today's data-driven world. Whether you are a data scientist, analyst, or business leader, understanding the principles of data mining can help you unlock the potential of your data and drive success in your organization.。
Improving tax administration with data mining
Daniele Micci-Barreca, PhD, and Satheesh Ramachandran, PhD Elite Analytics, LLCExecutive reportImproving tax administration with data miningTable of contentsIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2Why data mining? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3What is data mining? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3Case study: An audit selection strategy for the State of Texas . . . . . . . . . . . . . . . . . . . . . . .5Other data mining applications in tax administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12About Clementine ® . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12About SPSS Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12About Elite Analytics, LLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13IntroductionBoth federal and state tax administration agencies must use their limited resources to achieve maximal taxpayer compliance. The purpose of this white paper is to show how data mining helps tax agencies achieve compliance goals and improve operating efficiency using their existing resources. The paper begins with an overview of data mining and then details an actual tax compliance application implemented by Elite Analytics, LLC, a data mining consulting services firm and SPSS Inc. partner, for the Audit Division of the Texas Comptroller of Public Accounts(CPA). The paper concludes with an overview of additional applications of data mining technology in the area of tax compliance.Tax agencies primarily use audits to ensure compliance with tax laws and maintain associated revenue streams. Audits indirectly drive voluntary compliance and directly generate additional tax collections, both of which help tax agencies reduce the “tax gap” between the tax owed and the amount collected. Audits, therefore, are critical to enforcing tax laws and helping tax agencies achieve revenue objectives, ensuring the fiscal health of the country and individual states.Managing an effective auditing organization involves many decisions. What is the best audit selection strategy or combination of strategies? Should it be based on reported tax amounts or on the industry type? How should agencies allocate audit resources among different tax types? Some tax types may yield greater per-audit adjustments. Others may be associated with a higher incidence of noncompliance. An audit is a process with many progressive stages, from audit selection and assignment to hearings, adjudication, and negotiation, to collection and, in some cases, enforcement. Each stage involves decisions that can increase or reduce the efficiency of the overall auditing program.Audit selection methods range from simple random selection to more complex rule-based selection based on “audit flags,”to sophisticated statistical and data mining selection techniques. Selection strategies can vary by tax type, and even within a single type. Certain selection strategies, for example, segment taxpayers within a specific tax type, and then apply different selection rules to each segment. Texas categorizes taxpayers that account for the top 65 percent of the state’s sales tax collections as Priority One accounts, and audits these accounts every four years. Texas also audits Prior Productive taxpayers—accounts that yielded tax adjustments greater than $10,000 in previous audits.As in many states, Texas’ taxpayer population has risen steadily over the last decade, without any proportionate rise in auditing resources. As a result, Texas and several other states, as well as tax agencies in the United Kingdom and Australia, rely on data mining to help find delinquent taxpayers and make effective resource allocation decisions. Data mining leverages specialized data warehousing systems that integrate internal and external data sources to enable a variety of applications,from trend analysis to non-compliance detection and revenue forecasting, that help agencies answer questions such as:■How should we split auditing resources among tax types?■Which taxpayers are higher audit priorities?■What is the expected yield from a particular audit type?■Which SIC codes are associated with higher rates of noncompliance?Why data mining?Tax agencies have access to enormous amounts of taxpayer data. Most auditing agencies, in fact, draw information from these data sources to support auditing functions. Audit selectors, for example, search data sources for taxpayers with specific profiles. These profiles, developed by experts, may be based on a single attribute, such the taxpayer industry code (SIC), or on a complex combination of attributes (for example, taxpayers in a specific retail sector that have a specific sales-to-reported-tax ratio). Data mining technologies do the same thing, but on a much bigger scale. Using data mining techniques, tax agencies can analyze data from hundreds of thousands of taxpayers to identify common attributes and then create profiles that represent different types of activity. Agencies, for example, can create profiles of high-yield returns, so auditors can concentrate resources on new returns with similar attributes. Data mining enables organizations to leverage their data to understand, analyze, and predict noncompliant behavior.What is data mining?Data mining is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules [1]. Organizations use this information to detect existing fraud and noncompliance,and to prevent future occurrences.Data mining also enables data exploration and analysis without any specific hypothesis in mind, as opposed to traditional statistical analysis, in which experiments are designed around a particular hypothesis. While this openness adds a strong exploratory aspect to data mining projects, it also requires that organizations use a systematic approach in order to achieve usable results. The CRoss-Industry Standard Process for Data Mining (CRISP-DM) (see Figure 1) was developed in 1996 by a consortium of data mining consultants and technology specialists that included SPSS, Daimler-Benz (now DaimlerChrysler) and NCR [2]. CRISP-DM’s developers relied on their real-world experience to develop a six-phase process that incorporates an organization’s business goals and knowledge. CRISP-DM is considered the de facto standard for the data mining industry.The six phases of CRISP-DM Business understandingThe first phase ensures that all participants understand the project goals from a business or organizational perspective. These business goals are then incorporated in a data mining problem definition and detailed project plan. For a tax auditing agency, this would involve understanding the audit management process,the role and functions performed, the information that is collected and managed, and the specific challenges to improving audit efficiency. This information would be incorporated into the data mining problemdefinition and project plan.Figure 1: The CRoss-Industry Standard Process for Data Mining (CRISP-DM)Data understandingThe second phase is designed to assess the sources, quality, and characteristics of the data. This initial exploration can also provide insights that help to focus the project. The result is a detailed understanding of the key data elements that will be used to build models. This phase can be time-consuming for tax agencies that have many data sources, but it is critically important to the project.Data preparationThe next phase involves placing the data in a format suitable for building models. The analyst uses the business objectives determined in the business understanding step to determine which data types and data mining algorithms to use. This phase also resolves data issues uncovered in the data understanding phase, such as missing data.ModelingThe modeling phase involves building the data mining algorithms that extract the knowledge from the data. There are a variety of data mining techniques; each is suitable for discovering a specific type of knowledge. A tax agency would use classification or regression models, for example, to discover the characteristics of more productive tax audits. Each technique requires specific types of data, which may require a return to the data preparation phase. The modeling phase produces a model or a set of models containing the discovered knowledge in an appropriate format.EvaluationThis phase focuses on evaluating the quality of the model or models. Data mining algorithms can uncover an unlimited number of patterns; many of these, however, may be meaningless. This phase helps determine which models are useful in terms of achieving the project’s business objectives. In the context of audit selection, a predictive model for audit outcome would be assessed against a benchmark set of historical audits for which the outcome is known.DeploymentIn the deployment phase, the organization incorporates the data mining results into the day-to-day decision-making process. Depending on the significance of the results, this may require only minor modifications, or it may necessitate a major reengineering of processes and decision-support systems. The deployment phase also involves creating a repeatable process for model enhancements or recalibrations. Tax laws, for example, are likely to change over time. Analysts need a standard process for updating the models accordingly and deploying new results.The appropriate presentation of results ensures that decision makers actually use the information. This can be as simple as creating a report or as complex as implementing a repeatable data mining process across the enterprise. It is important that project managers understand from the beginning what actions they will need to take in order to make use of the final models.The six phases described in this paper are integral to every data mining project. Though each phase is important, the sequence is not rigid; certain projects may require you to move back and forth between phases. The next phase or the next task in a phase depends on the outcome of each of the previous phases. The inner arrows in Figure 1indicate the most important and frequent dependencies between phases. The outer circle symbolizes the cyclical nature of data mining projects, namely that lessons learned during a data mining project and after deployment can trigger new, more focused business questions. Subsequent data mining projects, therefore, benefit from experience gained in previous ones.Eighty to 90 percent of a typical data mining project is spent on phases other than modeling, and the success of the models depends heavily on work performed in these phases. To create the models, the analyst typically uses a collection of techniques and tools. Data mining techniques come from a variety of disciplines, including machine learning, statistical analysis, pattern recognition, signal processing, evolutionary computation, and pattern visualization. A detailed discussion of these methods is beyond the scope of this paper. The next sections, however, discuss data mining methods relevant to tax audit applications.Case study: An audit selection strategy for the State of TexasAs previously mentioned, there are many audit selection methods, each of which results in different levels of productivity. Measured in terms of dollars recovered per audit hour, the relationship between productivity and the audit selection strategy is quantifiable. This section focuses on the Audit Select scoring system implemented by Elite Analytics for the Audit Division of the Texas Comptroller of Public Accounts, and how this approach compares to traditional audit selection strategies used by the division.Already the data mining tool of choice for the Audit Division, Clementine was used throughout the implementation of the new audit selection strategy. According to Elite Analytics, “Not only did we find the variety of data preparation, modeling, and deployment features unmatched in any other product, but the Clementine workbench made working and categorizing completed work within the CRISP-DM model very intuitive.”Predictive modelingPredictive modeling is one of the most frequently used forms of data mining. As its name suggests, predictive modeling enables organizations to predict the outcome of a given process and use this insight to achieve a desired outcome. In terms of audit selection, the goal is to predict which audit leads are more likely to yield greater tax adjustments.Predictive models typically produce a numeric score that indicates the likely outcome of an audit. A high score, for instance, indicates that the tax adjustment from that audit is likely to be high or above average, while a low score indicates a low likelihood of a large tax adjustment. A real-world example is the Discriminant Index Function (DIF) score developed by the IRS to identify returns with a high probability of unreported income. Use of the DIF score results in significantly higher tax assessments than do purely random audits[3].Scoring models have become more popular, due in large part to their ability to manage hundreds of variables and large populations. Among other applications, organizations use scoring models to:■Assess credit risk■Identify fraudulent credit card transactions■Determine the response potential of mailing lists■Rank prospects in terms of buying potentialUsing models to predict sales tax audit outcomesThe Audit Division’s Advanced Database System (ADS) is a data warehouse designed to support tax compliance applications such as the Audit Select system, which uses predictive models to identify sales tax audit leads. Figure 2depicts the process of building and using predictive models for audit selection.The first step is to calibrate or train the model using a training set of historical audit data with a known outcome. This enables the model to “learn” the relationship between taxpayer attributes and the audit outcomes. The Audit Select scoring model uses the following five sources of data to create a taxpayer profile (see Figure 3):■Business information, such as taxpayer SIC code, business type (corporation, partnership, etc.), and location■Sales tax filings from the most recent four years of sales tax reports■Other tax filings, primarily for franchise tax■Employee and wage information reported to another state agency■Prior audit outcomesOnce trained, the model can be applied to the entire taxpayer population in a process known as generalization. The model generalizes what it learns from historical audits to analyze new returns and assign Audit Select scores(see examples in Figure 4). Human audit selectors then use the Audit Select scores to determine which businesses to audit.The model relies on extensive data preparation and transformations that map the raw data into more informative indicators. For example, while the actual and relative (compared to gross sales) amount of deduction is a good raw indicator, it is useful to compare the figures to those of similar businesses. This peer group comparison is used throughout the model to develop additional indicators.Figure 4Figure 4: Training and scoring examplesIn Figure 4, the first table represents the training set of historical audits used to calibrate the model. The second table shows the model’s predictive scores for the current population of taxpayers.Results from the Audit Select scoring systemThe most effective way to assess a scoring system is to compare the scores it produces to actual outcomes. In the context of audit selection, the goal is to measure the difference between the final tax adjustment and the score assigned by the model.The chart in Figure 5shows the average tax adjustment for Audit Select scores between one and 1000. The average tax adjustment, as expected, increases in proportion to the scores. The chart shows, for example, that audits performed on taxpayers with scores below 100 resulted in an average tax adjustment of$3,300. In contrast, audits performed on taxpayers with scores above 900 resulted in an average tax adjustment of$78,000. While the percentage of taxpayers with scores higher than 900 is smaller than the percentage with scores below 100, additional research in this study revealed that only 57 percent of the current population scoring 900 or greater has been audited at least once. This taxpayer segment clearly represents a significant pool of audit candidates.Comparison with other selection strategiesAs demonstrated in the examples above, the data mining and predictive modeling techniques used by the Audit Select scoring system add a new level of sophistication to the audit selection process. The most accurate measure of a new technology, however, is not technical complexity or theoretical soundness, but the degree to which it improves an existing process. This measurement is of particular importance when less sophisticated, yet well-tested techniques are already in use.Other selection strategiesBefore evaluating the performance of the Audit Select scoring system, therefore, it is important to review the selection strategies that the State of Texas used for many years and continues to use alongside the new system.■Priority One: The State of Texas classifies all businesses that contribute to the top 65 percent of tax dollars collected as Priority One taxpayers. The state audits these taxpayers approximately every four years. The Priority One selection strategy uses a very simple scoring and ranking algorithm. The score is the relative percentage of sales tax dollars contributed to the state’s total collection. The state ranks taxpayers by score and then applies a threshold to produce the target list. There are similarities between the Priority One and Audit Select selection strategies, but the main difference is the logic behind the score. The Priority One score is one-dimensional; it considers only the reported tax amount. The Audit Select score, on the other hand, takes into account a number of taxpayer attributes(as depicted in Figure 3).■Prior Productive: This strategy automatically selects taxpayers with prior audit adjustments of more than $10,000. Aswith Priority One, the Prior Productive strategy uses only one selection criterion, which is one of several used by theAudit Select system.The average tax adjustment for Priority One audits is approximately$76,000 (median $9,600). This outcome is significantly higher than the $12,000 (median $1,300) average for non-Priority One audits and the $18,000 (median $1,600) average for all sales tax audits. Priority One audits also represent approximately nine percent of all Texas sales tax audits.Audit Select versus Priority OneWhen comparing selection methods, it is important to consider not only the average tax adjustment of each method, but also the volume of audits. This is particularly important when assessing a score-based solution such as the Audit Select system. In fact, while the score enables ranking of potential targets, each score range produces a different number of candidates. When comparing the Priority One criterion with the score criterion, it is therefore important to use a score range that produces an equal number of audits. In this case, we compare the average outcome of the top nine percent of all audits, based on the score, with the average outcome of Priority One audits(which represent nine percent of the audits).Based on scores produced by the Audit Select model, the average tax adjustment for the top nine percent of audits is approximately$88,000 (median $16,000) (see Figure 6). This is a 16 percent improvement over the Priority One average adjustment(65 percent improvement over the median adjustment) on an equal number of audits. Note that36 percent ofthe top nine percent of the Audit Select audits is made up of Priority One audits, while the remaining 64 percent would not have been selected by the Priority One strategy. This demonstrates that the Audit Select strategy is not necessarily orthogonal(or in contrast) to Priority One. The Audit Select score can, in fact, be used to improve on the Priority One strategyby further segmenting the Priority One population and eliminating audits likely to result in small tax adjustments.Audit Select versus Prior ProductiveAs with the previous comparison, there are significant differences in the results obtained by the Audit Select and Prior Productive selection strategies (see Figure 7). Approximately 20 percent of the audits performed fall within the Prior Productive selection criteria. Five percent of these, however, would also be selected by the Priority One strategy. Excluding those cases, the average tax adjustment for Prior Productive audits was $17,700 (median $4,100). In comparison, the top 20 percent of the audits, based on the Audit Select score (and excluding potential Priority One audits), shows an average adjustment of$27,000 (median $6,500).Model evolutionData mining has the most impact when it becomes an integral part of any system or application. Data mining models are rarely static. As more data sources are available and processes change, the models evolve. User feedback is also a critical component in model evolution.Since its implementation, the Audit Select scoring model has evolved significantly, and additional enhancements are currently in progress. Several factors contribute to the evolution of a system of this kind. First and foremost is the evolution of the data sources. The usefulness and accuracy of any model is determined primarily by the quality, quantity, and richness of the data available. The second factor is domain knowledge, which itself evolves to reflect emerging trends and changes in the data generation process. The final factor is technological improvements: More sophisticated modeling algorithms, data transformation strategies, and model segmentation techniques enable significantly improved end results.Other data mining applications in tax administrationAudit selection is one of many possible data mining applications in tax administration. In the area of tax compliance alone, tax collectors face diverse challenges, from underreporting to nonreporting to enforcement. Data mining offers many valuable techniques for increasing the efficiency and success rate of tax collection. Data mining also leverages the variety and quantity of internal and external data sources available in modern data warehousing systems. Here are just a few of the many potential uses of data mining in the context of tax compliance:Outlier-based selectionThis is an alternative approach to audit selection that uses specific data mining algorithms to build profiles of typical behaviors and then select taxpayers that do not match the profiles. This modeling process involves creating valid taxpayer segments, characterizing the normative taxpayer profile for each segment, and creating the rules for outlier detection. Though this method is similar to that applied by many audit selectors on a daily basis, the added benefit of the data mining approach is its ability to process large amounts of data and analyze multiple taxpayer characteristics simultaneously.Lead prioritizationMany tax agencies flag potential nonfilers by, for example, cross-matching external data with internal lists of current filers. This type of application often produces many leads, each of which must be manually verified and pursued. Given the limited staff available to follow up on leads, it is important to develop criteria for prioritizing the leads. Data mining, however, can predict the tax dollars owed by an organization or individual taxpayer by modeling the relationship between the attributes and reported tax for known taxpayers.Profiling for cross-tax affinityAgencies can use data mining to analyze existing taxpayers for associations between the types of businesses and the tax types(more than 30 in Texas) for which the taxpayers file. Co-occurrence of certain tax types may infer liability for another tax type for which the taxpayer did not file.Workflow analysisFollowing up on leads can be a lengthy process consisting of multiple mail exchanges between the tax agency and the organizations classified as potential nonfilers. Tracking this process, however, generates data that is useful for modeling the process. The process models can then be used to predict the value of current pipelines and to optimize resource allocation.Anomaly detectionTaxpayers self-report several important attributes(SIC, organization type, etc.). Due to unavoidable data entry errors, however, some taxpayers are categorized incorrectly or even uncategorized. Since these attributes and categories drive audit selection and other processes, it is always a good idea to apply data mining’s rule induction techniques to detect errors and anomalies.Economic models for optimal targetingIn most traditional targeting applications, such as target marketing or e-commerce fraud detection, there is a tradeoff between the number of audits to target and the cost per audit. When audit cost information is available, agencies can use data mining to develop targeting strategies that maximize overall collections.ConclusionAs outlined in this paper, data mining has many existing and potential applications in tax administration. In the case of the Audit Division of the Texas Comptroller of Public Accounts, predictive modeling enables the agency to identify noncompliant taxpayers more efficiently and effectively, and to focus auditing resources on the accounts most likely to produce positive tax adjustments. This helps the agency make better use of its human and other resources, and minimizes the financial burden on compliant taxpayers. Data mining also helps the agency refine its traditional audit selection strategies to produce more accurate results.About ClementineClementine is a data mining workbench that enables organizations to quickly develop predictive models using business expertise, and then deploy them to improve decision making. Clementine is widely regarded as the leading data mining workbench because it delivers the maximum return on data investment in the minimum amount of time. Unlike other data mining workbenches, which focus merely on models for enhancing performance, Clementine supports the entire data mining process to reduce time-to-solution. And Clementine is designed around the de facto industry standard for data mining—CRISP-DM. CRISP-DM makes data mining a business process by focusing data mining technology on solving specific business problems.About SPSS Inc.SPSS Inc. [NASDAQ: SPSS] is the world's leading provider of predictive analytics software and services. The company’s predictive analytics technology connects data to effective action by drawing reliable conclusions about current conditions and future events. More than 250,000 commercial, academic,and public sector organizations rely on SPSS technology to help increase revenue, reduce costs, improve processes, and detect and prevent fraud. Founded in 1968, SPSS is headquartered in Chicago, Illinois. To learn more, please visit . For SPSS office locations and telephone numbers, go to /worldwide.。
我所知道的一点DataMining-电子邮件系统
◎我所知道的一點Data Mining1.前言2.定義3.方法4.工具5.應用6.結論◎以上內容提供者:趙民德中央研究院統計科學研究所◎◎資料採礦（Data Mining）連載之一‧何謂DATA MINING‧DATA MINING和統計分析的不同‧為什麼需要DATA MINING何謂DATA MINING？資料採礦的工作（Data Mining）是近年來資料庫應用領域中，相當熱門的議題。
它是個神奇又時髦的技術，但卻也不是什麼新東西，因為Data Mining使用的分析方法，如預測模型（迴歸、時間數列）、資料庫分割（Database Segmentation）、連接分析（Link Analysis）、偏差偵測（Deviation Detection）等；美國政府從第二次世界大戰前，就在人口普查以及軍事方面使用這些技術，但是資訊科技的進展超乎想像，新工具的出現，例如關連式資料庫、物件導向資料庫、柔性計算理論（包括Neural network、Fuzzy theory、Genetic Algorithms、Rough Set等）、人工智慧的應用（如知識工程、專家系統），以及網路通訊技術的發展，使從資料堆中挖掘寶藏，常常能超越歸納範圍的關係；使Data Mining成為企業智慧的一部份。
Data Mining是一個浮現中的新領域。
在範圍和定義上、推理和期望上有一些不同。
挖掘的資訊和知識從巨大的資料庫而來，它被許多研究者在資料庫系統和機器學習（Machine learning）當作關鍵研究議題，而且也被企業體當作主要利基的重要所在。
有許多不同領域的專家，對Data Mining展現出極大興趣，例如在資訊服務業中，浮現一些應用，如在Internet之資料倉儲和線上服務，並且增加企業的許多生機。
隨著資訊科技的進步以及電子化時代的來臨，現今企業所面對的是一個與以往截然不同的競爭環境。
在資訊科技的推波助瀾下，不僅企業競爭的強度與速度倍數於以往，激增的市場交易也使得各企業所需儲存與處理的資料量越來越龐大。
Data-Mining-OverviewPPT课件
Extract interesting and useful knowledge from the data Find rules, regularities, irregularities, patterns, constraints hopefully, this will help us better compete in business, do research, learn
Data collection and data availability
Automated data collection tools, database systems, Web, computerized society
Major sources of abundant data
Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, images, video, documents, ….
Knowledge
Information read, heard or seen and understood and
integrated
Wisdom
Distilled knowledge and understanding which can
lead to decisions
4
What is Data Mining
which products in the catalog often sell together market segmentation (find groups of customers/users with similar
数据挖掘(Data Mining)
数据挖掘（Data Mining）DM：数据挖掘（Data Mining）KDD：知识发现（Knowledge Discovery in Databases）一、背景1、目前的数据库系统虽然可以高效地实现数据的录入、查询、统计等功能，但无法发现数据中存在的关系和规则2、数据十分丰富，而信息相当贫乏。
3、数据坟墓二、数据挖掘的定义1、数据挖掘是从大量数据中提取或“挖掘”知识2、数据挖掘（Data Mining）就是从大量的、不完全的、有噪声的、模糊的、随机的实际应用数据中，提取隐含在其中的、人们事先不知道的、但又是潜在有用的信息和知识的过程n所谓基于数据库的知识发现3、所谓基于数据库的知识发现（KDD）是指从大量数据中提取有效的、新颖的、潜在有用的、最终可被理解的模式的非平凡过程。
OLAP【联机分析处理】面向主题的，主要面向公司领导者；OLTP【联机事务处理】面向应用的，主要面向公司职员。
OLAP是验证型的，建立在数据仓库的基础上；数据挖掘是挖掘型的，建立在各种数据源的基础上三、数据挖掘工具：DBMiner、Admocs、Predictive－CRM、SAS/EM（Enterprise Miner）、Weka目前，世界上比较有影响的典型数据挖掘系统包括：•SAS公司的Enterprise Miner•IBM公司的Intelligent Miner•SGI公司的SetMiner•SPSS公司的Clementine•Sybase公司的Warehouse Studio•RuleQuest Research公司的See5•还有CoverStory、EXPLORA、Knowledge Discovery Workbench、DBMiner、Quest等。
四、KDD过程在上述步骤中，数据挖掘占据非常重要的地位，它主要是利用某些特定的知识发现算法，在一定的运算效率范围内，从数据中发现出有关知识，决定了整个KDD过程的效果与效率。
Data Mining： What is Data Mining？
Data Mining: What is Data Mining? OverviewGenerally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.【一般来说，数据挖掘（有时也被称为数据或知识发现）是从不同的角度进行分析和总结数据转化为有用信息的信息的过程。
可以用来增加收入，降低成本，或两者兼而有之。
数据挖掘软件是众多数据分析工具之一。
它允许用户分析来自许多不同的层面或角度的数据，归类，发现和总结的关系。
从技术上讲，数据挖掘是在多个领域的大型关系数据库中发现相互关系或模式的过程。
】Continuous InnovationAlthough data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.【不断创新虽然数据挖掘是一个相对较新的术语，但其技术不是。
6-data mining(1)
Part II Data MiningOutlineThe Concept of Data Mining(数据挖掘概念) Architecture of a Typical Data Mining System (数据挖掘系统结构)What can be Mined? (能挖掘什么？)Major Issues(主要问题)in Data MiningData Cleaning(数据清理)3What Is Data Mining?Data mining is the process of discovering interesting knowledge from large amounts of data. (数据挖掘是从大量数据中发现有趣知识的过程) The main difference that separates information retrieval apart from data mining is their goals. (数据挖掘和信息检索的主要差别在于他们的目标) Information retrieval is to help users search for documents or data that satisfy their information needs(信息检索帮用户寻找他们需要的文档/数据)e.g. Find customers who have purchased more than $10,000 in the last month .(查找上个月购物量超过1万美元的客户)Data mining discovers useful knowledge by analyzing data correlations using sophisticated data mining techniques(数据挖掘用复杂技术分析…)e.g. Find all items which are frequently purchased with milk .(查找经常和牛奶被购买的商品)A KDD Process (1) Some people view data mining as synonymous5A KDD Process (2)Learning the application domain (学习应用领域相关知识):Relevant knowledge & goals of application (相关知识和目标) Creating a target data set (建立目标数据集) Data selection, Data cleaning and preprocessing (预处理)Choosing functions of data mining (选择数据挖掘功能)Summarization, classification, association, clustering , etc.Choosing the mining algorithm(s) (选择挖掘算法)Data mining (进行数据挖掘): search for patterns of interest Pattern evaluation and knowledge presentation (模式评估和知识表示)Removing redundant patterns, visualization, transformation, etc.Present results to user in meaningful manner.Use of discovered knowledge (使用所发现的知识)7Concept/class description (概念/类描述)Characterization(特征): provide a summarization of the given data set Comparison(区分): mine distinguishing characteristics(挖掘区别特征)that differentiate a target class from comparable contrasting classes. Association rules (correlation and causality)(关联规则)Association rules are of the form(这种形式的规则): X ⇒Y,Examples: contains(T, “computer”) ⇒contains(T, “software”)[support = 1%, confidence = 50%]age(X, “20..29”) ∧income(X, “20..29K ”) ⇒buys(X, “PC ”)[support = 2%, confidence = 60%]Classification and Prediction (分类和预测)Find models that describe and distinguish classes for future prediction.What kinds of patterns can be mined?(1)What kinds of patterns can be mined?(2)Cluster(聚类)Group data to form some classes(将数据聚合成一些类)Principle: maximizing the intra-class similarity and minimizing the interclass similarity (原则: 最大化类内相似度，最小化类间相似度) Outlier analysis: objects that do not comply with the general behavior / data model. (局外者分析: 发现与一般行为或数据模型不一致的对象) Trend and evolution analysis (趋势和演变分析)Sequential pattern mining(序列模式挖掘)Regression analysis(回归分析)Periodicity analysis(周期分析)Similarity-based analysis(基于相似度分析)What kinds of patterns can be mined?(3)In the context of text and Web mining, the knowledge also includes: (在文本挖掘或web挖掘中还可以发现)Word association (术语关联)Web resource discovery (WEB资源发现)News Event (新闻事件)Browsing behavior (浏览行为)Online communities (网上社团)Mining Web link structures to identify authoritative Web pages finding spam sites (发现垃圾网站)Opinion Mining (观点挖掘)…10Major Issues in Data Mining (1)Mining methodology(挖掘方法)and user interactionMining different kinds of knowledge in DBs (从DB 挖掘不同类型知识) Interactive mining of knowledge at multiple levels of abstraction (在多个抽象层上交互挖掘知识)Incorporation of background knowledge (结合背景知识)Data mining query languages (数据挖掘查询语言)Presentation and visualization of data mining results(结果可视化表示) Handling noise and incomplete data (处理噪音和不完全数据) Pattern evaluation (模式评估)Performance and scalability (性能和可伸缩性) Efficiency(有效性)and scalability(可伸缩性)of data mining algorithmsParallel(并行), distributed(分布) & incremental(增量)mining methods©Wu Yangyang 11Major Issues in Data Mining (2)Issues relating to the diversity of data types (数据多样性相关问题)Handling relational and complex types of data (关系和复杂类型数据) Mining information from heterogeneous databases and www(异质异构) Issues related to applications (应用相关的问题) Application of discovered knowledge (所发现知识的应用)Domain-specific data mining tools (面向特定领域的挖掘工具)Intelligent query answering (智能问答) Process control(过程控制)and decision making(决策制定)Integration of the discovered knowledge with existing knowledge:A knowledge fusion problem (知识融合)Protection of data security(数据安全), integrity(完整性), and privacy12CulturesDatabases: concentrate on large-scale (non-main-memory) data.(数据库：关注大规模数据)To a database person, data-mining is an extreme form of analytic processing. Result is the data that answers the query.(对数据库工作者而言数据挖掘是一种分析处理, 其结果就是问题答案) AI (machine-learning): concentrate on complex methods, small data.(人工智能(机器学习)：关注复杂方法，小数据)Statistics: concentrate on models. (统计：关注模型.)To a statistician, data-mining is the inference of models. Result is the parameters of the model (数据挖掘是模型推论, 其结果是一些模型参数)e.g. Given a billion numbers, a statistician might fit the billion points to the best Gaussian distribution and report the mean and standard deviation.©Wu Yangyang 13Data Cleaning (1)Data Preprocessing (数据预处理):Cleaning, integration, transformation, reduction, discretization (离散化) Why data cleaning? (为什么要清理数据？)--No quality data, no quality mining results! Garbage in, Garbage out! Measure of data quality (数据质量的度量标准)Accuracy (正确性)Completeness (完整性)Consistency(一致)Timeliness(适时)Believability(可信)Interpretability(可解释性) Accessibility(可存取性)14Data Cleaning (2)Data in the real world is dirtyIncomplete (不完全)：Lacking some attribute values (缺少一些属性值)Lacking certain interest attributes /containing only aggregate data(缺少某些有用属性或只包含聚集数据)Noisy(有噪音): containing errors or outliers(包含错误或异常) Inconsistent: containing discrepancies in codes or names(不一致: 编码或名称存在差异)Major tasks in data cleaning (数据清理的主要任务)Fill in missing values (补上缺少的值)Identify outliers(识别出异常值)and smooth out noisy data(消除噪音)Correct inconsistent data(校正不一致数据) Resolve redundancy caused by data integration (消除集成产生的冗余)15Data Cleaning (3)Handle missing values (处理缺值问题) Ignore the tuple (忽略该元组) Fill in the missing value manually (人工填补) Use a global constant to fill in the missing value (用全局常量填补) Use the attribute mean to fill in the missing value (该属性平均值填补) Use the attribute mean for all samples belonging to the same class to fill in the missing value (用同类的属性平均值填补) Use the most probable value(最大可能的值)to fill in the missing value Identify outliers and smooth out noisy data(识别异常值和消除噪音)Binning method (分箱方法):First sort data and partition into bins (先排序、分箱)Then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.(然后用平均值、中值、边界值平滑)©Wu Yangyang 16Data Cleaning (4)Example: Sorted data: 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 Partition into (equi-depth) bins (分成等深的箱):-Bin 1: 4, 8, 9, 15-Bin 2: 21, 21, 24, 25-Bin 3: 26, 28, 29, 34Smoothing by bin means (用平均值平滑):-Bin 1: 9, 9, 9, 9-Bin 2: 23, 23, 23, 23-Bin 3: 29, 29, 29, 29Smoothing by bin boundaries (用边界值平滑):-Bin 1: 4, 4, 4, 15-Bin 2: 21, 21, 25, 25-Bin 3: 26, 26, 26, 34Clustering (。
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techniques is also provided, based on the kinds of databases to be mined, the kinds of knowledge to be discovered, and the kinds of techniques to be adopted. This survey is organized according to one classi cation scheme: the kinds of knowledge to be mined.
J. Han was supported in part by the research grant NSERC-A3723 from the Natural Sciences and Engineering Research Council of Canada, the research grant NCE:IRIS/Precarn-HMI5 from the Networks of Centres of Excellence of Canada, and research grants from MPR Teltech Ltd. and Hughes ResearBM T.J. Watson Res. Ctr.
P.O.Box 704 Yorktown, NY 10598, U.S.A.
Abstract
Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many di erent elds have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classi cation of the available data mining techniques is provided and a comparative study of such techniques is presented.
In response to such a demand, this article is to provide a survey on the data mining techniques developed in several research communities, with an emphasis on database-oriented techniques and those implemented in applicative data mining systems. A classi cation of the available data mining
Data Mining: An Overview from Database Perspective
Ming-Syan Chen Elect. Eng. Department National Taiwan Univ. Taipei, Taiwan, ROC
Jiawei Han School of Computing Sci. Simon Fraser University B.C. V5A 1S6, Canada
Data mining, which is also referred to as knowledge discovery in databases, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases 70]. There are also many other terms, appearing in some articles and documents, carrying a similar or slightly di erent meaning, such as knowledge mining from databases, knowledge extraction, data archaeology, data dredging, data analysis, and so on. By knowledge discovery in databases, interesting knowledge, regularities, or high-level information can be extracted from the relevant sets of data in databases and be investigated from di erent angles, and large databases thereby serve as rich and reliable sources for knowledge generation and veri cation. Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. The discovered knowledge can be applied to information management, query processing, decision making, process control, and many other applications. Researchers in many di erent elds, including database systems, knowledge-base systems, arti cial intelligence, machine learning, knowledge acquisition, statistics, spatial databases, and data visualization, have shown great interest in data mining. Furthermore, several emerging applications in information providing services, such as on-line services and World Wide Web, also call for various data mining techniques to better understand user behavior, to meliorate the service provided, and to increase the business opportunities.
Index Terms | Data mining, knowledge discovery, association rules, classi cation, data clustering, pattern matching algorithms, data generalization and characterization, data cubes, multiple-dimensional databases.
1 Introduction
Recently, our capabilities of both generating and collecting data have been increasing rapidly. The widespread use of bar codes for most commercial products, the computerization of many business and government transactions, and the advances in data collection tools have provided us with huge amounts of data. Millions of databases have been used in business management, government administration, scienti c and engineering data management, and many other applications. It is noted that the number of such databases keeps growing rapidly because of the availability of powerful and a ordable database systems. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance 30, 70, 76].