Data Mining Concepts and Techniques second edition 数据挖掘概念与技术 第二版 韩家炜 第十一章.PPT
数据挖掘概念与技术_课后题答案
数据挖掘概念与技术_课后题答案数据挖掘⼀⼀概念概念与技术Data MiningConcepts andTechniques习题答案第1章引⾔1.1什么是数据挖掘？在你的回答中，针对以下问题：1.2 1.6定义下列数据挖掘功能：特征化、区分、关联和相关分析、预测聚类和演变分析。
使⽤你熟悉的现实⽣活的数据库，给岀每种数据挖掘功能的例⼦。
解答：特征化是⼀个⽬标类数据的⼀般特性或特性的汇总。
例如，学⽣的特征可被提岀，形成所有⼤学的计算机科学专业⼀年级学⽣的轮廓，这些特征包括作为⼀种⾼的年级平均成绩（GPA: Grade point aversge）的信息，还有所修的课程的最⼤数量。
区分是将⽬标类数据对象的⼀般特性与⼀个或多个对⽐类对象的⼀般特性进⾏⽐较。
例如，具有⾼GPA的学⽣的⼀般特性可被⽤来与具有低GPA的⼀般特性⽐较。
最终的描述可能是学⽣的⼀个⼀般可⽐较的轮廓，就像具有⾼GPA的学⽣的75%是四年级计算机科学专业的学⽣，⽽具有低GPA的学⽣的65%不是。
关联是指发现关联规则，这些规则表⽰⼀起频繁发⽣在给定数据集的特征值的条件。
例如，⼀个数据挖掘系统可能发现的关联规则为：major（X, Computi ng scie nee” S own s（X, personalcomputer ” [support=12%, confid en ce=98%]其中，X是⼀个表⽰学⽣的变量。
这个规则指出正在学习的学⽣，12% （⽀持度）主修计算机科学并且拥有⼀台个⼈计算机。
这个组⼀个学⽣拥有⼀台个⼈电脑的概率是98% （置信度，或确定度）。
分类与预测不同，因为前者的作⽤是构造⼀系列能描述和区分数据类型或概念的模型（或功能），⽽后者是建⽴⼀个模型去预测缺失的或⽆效的、并且通常是数字的数据值。
它们的相似性是他们都是预测的⼯具：分类被⽤作预测⽬标数据的类的标签，⽽预测典型的应⽤是预测缺失的数字型数据的值。
聚类分析的数据对象不考虑已知的类标号。
Chapter 4 Data Mining Primitives, Languages, and System Architectures 数据挖掘：概念与技术 英文版教
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Data Mining: Concepts and Techniques
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Measurements of Pattern Interestingness
Association
Mine_Knowledge_Specification ::= mine associations [as pattern_name]
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Data Mining: Concepts and Techniques
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Syntax for specifying the kind of knowledge to be mined (cont.)
from relation(s)/cube(s) [where condition] in relevance to att_or_dim_list order by order_list group by grouping_list having condition
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Data Mining: Concepts and Techniques
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Data Mining: Concepts and Techniques
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Visualization of Discovered Patterns
Different backgrounds/usages may require different forms of representation E.g., rules, tables, crosstabs, pie/bar chart etc.
数据挖掘概念与技术原书第3版课后练习题含答案
数据挖掘概念与技术原书第3版课后练习题含答案前言《数据挖掘概念与技术》（Data Mining: Concepts and Techniques）是一本经典的数据挖掘教材，已经推出了第3版。
本文将为大家整理并提供第3版课后习题的答案，希望对大家学习数据挖掘有所帮助。
答案第1章绪论习题1.1数据挖掘的基本步骤包括：1.数据预处理2.数据挖掘3.模型评价4.应用结果习题1.2数据挖掘的主要任务包括：1.描述性任务2.预测性任务3.关联性任务4.分类和聚类任务第2章数据预处理习题2.3数据清理包括以下几个步骤：1.缺失值处理2.异常值检测处理3.数据清洗习题2.4处理缺失值的方法包括：1.删除缺失值2.插补法3.不处理缺失值第3章数据挖掘习题3.1数据挖掘的主要算法包括：1.决策树2.神经网络3.支持向量机4.关联规则5.聚类分析习题3.6K-Means算法的主要步骤包括：1.首先随机选择k个点作为质心2.将所有点分配到最近的质心中3.重新计算每个簇的质心4.重复2-3步，直到达到停止条件第4章模型评价与改进习题4.1模型评价的方法包括：1.混淆矩阵2.精确率、召回率3.F1值4.ROC曲线习题4.4过拟合是指模型过于复杂，学习到了训练集的噪声和随机变化，导致泛化能力不足。
对于过拟合的处理方法包括：1.增加样本数2.缩小模型规模3.正则化4.交叉验证结语以上是《数据挖掘概念与技术》第3版课后习题的答案，希望能够给大家的学习带来帮助。
如果大家还有其他问题，可以在评论区留言，或者在相关论坛等平台提出。
Data Mining：Concepts and Techniques
Types of Outliers (I)
Three kinds: global, contextual and collective outliers Global Outlier Global outlier (or point anomaly) Object is Og if it significantly deviates from the rest of the data set Ex. Intrusion detection in computer networks Issue: Find an appropriate measurement of deviation Contextual outlier (or conditional outlier) Object is Oc if it deviates significantly based on a selected context o Ex. 80 F in Urbana: outlier? (depending on summer or winter?) Attributes of data objects should be divided into two groups Contextual attributes: defines the context, e.g., time & location Behavioral attributes: characteristics of the object, used in outlier evaluation, e.g., temperature Can be viewed as a generalization of local outliers—whose density significantly deviates from its local area Issue: How to define or formulate meaningful context?
数据仓库与数据挖掘教学大纲
数据仓库与数据挖掘教学大纲一、课程介绍数据仓库与数据挖掘是现代信息技术领域的重要学科，本课程旨在介绍数据仓库和数据挖掘的基本概念、原理和方法，培养学生分析和处理大规模数据的能力，以及利用数据挖掘技术进行知识发现和决策支持的能力。
二、课程目标1. 理解数据仓库和数据挖掘的基本概念和原理。
2. 掌握数据仓库和数据挖掘的常用方法和技术。
3. 能够独立设计和实施数据仓库和数据挖掘项目。
4. 能够利用数据挖掘技术进行知识发现和决策支持。
三、教学内容和安排1. 数据仓库基础知识- 数据仓库的概念和特点- 数据仓库架构和组成- 数据仓库的设计和建模2. 数据挖掘基础知识- 数据挖掘的概念和任务- 数据挖掘的过程和方法- 数据挖掘的评估和应用3. 数据仓库与数据挖掘技术- 数据清洗和预处理- 数据集成和转换- 数据加载和存储- 数据仓库查询和分析- 数据挖掘算法和模型4. 数据挖掘应用案例- 市场营销数据分析- 社交网络分析- 金融风险预测- 医疗数据挖掘5. 实践项目在课程结束前，学生将组成小组进行一个实践项目，包括数据仓库的设计和搭建，以及数据挖掘任务的实施和结果分析。
四、教学方法1. 理论讲授：通过课堂讲解，介绍数据仓库与数据挖掘的基本概念、原理和方法。
2. 实践操作：通过实验和项目实践，让学生亲自操作和实施数据仓库和数据挖掘任务。
3. 讨论与交流：鼓励学生参与课堂讨论，分享自己的见解和经验，促进学生之间的交流与合作。
五、考核方式1. 平时成绩：包括课堂表现、实验报告和项目成果等。
2. 期末考试：考察学生对数据仓库与数据挖掘的理论知识的掌握程度。
3. 实践项目评估：评估学生在实践项目中的设计和实施能力。
六、参考教材1. Jiawei Han, Micheline Kamber, Jian Pei. "Data Mining: Concepts and Techniques." Morgan Kaufmann, 2011.2. Ralph Kimball, Margy Ross. "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling." Wiley, 2013.七、参考资源1. 数据挖掘工具：Weka, RapidMiner, Python等。
数据挖掘概念与技术英文版第二版课程设计
Data Mining: Concepts and Techniques, Second EditionCourse DesignIntroductionData mining is the process of discovering hidden patterns and knowledge from large amounts of data. It has become an essential tool for businesses and organizations to gn insights into customer behavior, optimize marketing strategies, and improve decision-making processes. This course is designed for students who are interested in learning the fundamental concepts and techniques of data mining.Course Objectives1.To understand the basic concepts and principles of datamining.2.To learn how to apply data mining techniques to real-worldproblems.3.To gn experience in using data mining software and tools.4.To explore advanced topics in data mining.Course OutlineWeek 1: Introduction to Data Mining•What is data mining?•Why is data mining important?•Data preprocessing•Sampling•Data explorationWeek 2: Classification•Decision trees•Nve Bayes•K-Nearest Neighbor (KNN)•Support Vector Machines (SVM) Week 3: Association Rule Mining•Market Basket Analysis•Apriori algorithm•FP-Growth algorithmWeek 4: Clustering•K-Means•Hierarchical clustering•DBSCANWeek 5: Evaluation and Validation•Cross-validation•Confusion matrix•Precision, recall, and F1-score•ROC curveWeek 6: Text Mining•Text preprocessing•Text representation•Topic modeling•Sentiment analysisWeek 7: Web Mining•Web scraping•PageRank algorithm•Link analysis•Web usage miningWeek 8: Advanced Topics•Deep learning for data mining•Time series analysis•Graph mining•Recommender systemsCourse Requirements•Attendance and active participation in class discussions and activities.•Completion of individual assignments and group projects.•Interactive group presentations.•Final examination.ConclusionThis course is designed to equip students with the foundational knowledge and practical skills in data mining. Through this course, students will learn how to employ various data mining techniques to solve real-world problems, explore advanced topics and applications of data mining, and gn hands-on experience in using data mining software and tools.。
Data Mining - Concepts and Techniques CH01
We are drowning in data, but starving for knowledge! Solution: Data warehousing and data mining
Data warehousing and on-line analytical processing Miing interesting knowledge (rules, regularities, patterns,
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Chapter 1. Introduction
Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems Major issues in data mining
Other Applications
Text mining (news group, email, documents) and Web mining
Stream data mining
DNA and bio-data analysis
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Data Mining: Concepts and Techniques
multidimensional summary reports
statistical summary information (data central tendency and variation)
Data Mining Concepts and Techniques
Data Mining: Concepts and Techniques— Slides for Textbook — — Chapter 4 —Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.caJanuary 17, 2001 Data Mining: Concepts and Techniques 1Chapter 4: Data Mining Primitives, Languages, and System ArchitecturesnData mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query language Architecture of data mining systems SummaryData Mining: Concepts and Techniques 2n nn nJanuary 17, 2001Why Data Mining Primitives and Languages?nWhat Defines a Data Mining Task ?Task-relevant data Type of knowledge to be mined Background knowledge Pattern interestingness measurements Visualization of discovered patternsData Mining: Concepts and Techniques 4nnnFinding all the patterns autonomously in a database? — unrealistic because the patterns could be too many but uninteresting Data mining should be an interactive process n User directs what to be mined Users must be provided with a set of primitives to be used to communicate with the data mining system Incorporating these primitives in a data mining query language n More flexible user interaction n Foundation for design of graphical user interface n Standardization of data mining industry and practiceData Mining: Concepts and Techniques 3n n n n nJanuary 17, 2001January 17, 2001Task-Relevant Data (Minable View)Database or data warehouse name Database tables or data warehouse cubes Condition for data selection Relevant attributes or dimensions Data grouping criteriaData Mining: Concepts and Techniques 5 n nTypes of knowledge to be minedCharacterization Discrimination Association Classification/prediction Clustering Outlier analysis Other data mining tasksData Mining: Concepts and Techniques 6nnn n nnnn nnJanuary 17, 2001January 17, 20011Background Knowledge: Concept HierarchiesnMeasurements of Pattern InterestingnessnnnnSchema hierarchy n E.g., street < city < province_or_state < country Set-grouping hierarchy n E.g., {20-39} = young, {40-59} = middle_aged Operation-derived hierarchy n email address: login-name < department < university < country Rule-based hierarchy n low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 - P2) < $50Data Mining: Concepts and Techniques 7nnnSimplicity e.g., (association) rule length, (decision) tree size Certainty e.g., confidence, P(A|B) = n(A and B)/ n (B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc. Utility potential usefulness, e.g., support (association), noise threshold (description) Novelty not previously known, surprising (used to remove redundant rules, e.g., Canada vs. Vancouver rule implication support ratioData Mining: Concepts and Techniques 8January 17, 2001January 17, 2001Visualization of Discovered PatternsnChapter 4: Data Mining Primitives, Languages, and System ArchitecturesnDifferent backgrounds/usages may require different forms of representationnData mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query language Architecture of data mining systems SummaryData Mining: Concepts and Techniques 10E.g., rules, tables, crosstabs, pie/bar chart etc. Discovered knowledge might be more understandable when represented at high level of abstraction Interactive drill up/down, pivoting, slicing and dicing provide different perspective to datan nnConcept hierarchy is also importantnnn n9nDifferent kinds of knowledge require different representation: association, classification, clustering, etc.Data Mining: Concepts and TechniquesJanuary 17, 2001January 17, 2001A Data Mining Query Language (DMQL)nSyntax for DMQLnMotivationnA DMQL can provide the ability to support ad-hoc and interactive data mining By providing a standardized language like SQLnSyntax for specification ofn n n n ntask-relevant data the kind of knowledge to be mined concept hierarchy specification interestingness measure pattern presentation and visualizationnHope to achieve a similar effect like that SQL has on relational database Foundation for system development and evolution Facilitate information exchange, technology transfer, commercialization and wide acceptancen nnDesignnDMQL is designed with the primitives described earlierData Mining: Concepts and Techniques 11nPutting it all together — a DMQL queryData Mining: Concepts and Techniques 12January 17, 2001January 17, 20012Syntax for task-relevant data specificationnSpecification of task-relevant datause database database_name, or use data warehouse data_warehouse_name from relation(s)/cube(s) [where condition] in relevance to att_or_dim_list order by order_list group by grouping_list having conditionData Mining: Concepts and Techniques 13 January 17, 2001 Data Mining: Concepts and Techniques 14n n n n nJanuary 17, 2001Syntax for specifying the kind of knowledge to be minednSyntax for specifying the kind of knowledge to be mined (cont.)vnnCharacterization Mine_Knowledge_Specification ::= mine characteristics [as pattern_name] analyze measure(s) Discrimination Mine_Knowledge_Specification ::= mine comparison [as pattern_name] for target_class where target_condition {versus contrast_class_i where contrast_condition_i} analyze measure(s) Association Mine_Knowledge_Specification ::= mine associations [as pattern_name]Data Mining: Concepts and Techniques 15Classification Mine_Knowledge_Specification ::= mine classification [as pattern_name] analyze classifying_attribute_or_dimension Mine_Knowledge_Specification ::= mine prediction [as pattern_name] analyze prediction_attribute_or_dimension {set {attribute_or_dimension_ value_i}} i=v PredictionJanuary 17, 2001January 17, 2001Data Mining: Concepts and Techniques16Syntax for concept hierarchy specificationnSyntax for concept hierarchy specification (Cont.)nnTo specify what concept hierarchies to use use hierarchy <hierarchy> for <attribute_or_dimension> We use different syntax to define different type of hierarchies n schema hierarchies define hierarchy time_hierarchy on date as [date,month quarter,year] n set -grouping hierarchies define hierarchy age_hierarchy for age on customer as level1: {young, middle_aged, senior} < level0: all level2: {20, ..., 39} < level1: young level2: {40, ..., 59} < level1: middle_aged level2: {60, ..., 89} < level1: seniorData Mining: Concepts and Techniques 17noperation-derived hierarchies define hierarchy age_hierarchy for age on customer as {age_category(1), ..., age_category(5)} := cluster(default, age, 5) < all(age) rule-based hierarchies define hierarchy profit_margin_hierarchy on item as level_1: low_profit_margin < level_0: all if (price - cost)< $50 level_1: medium-profit_margin < level_0: all if ((price - cost) > $50) and ((price - cost) <= $250)) level_1: high_profit_margin < level_0: all if (price - cost) > $250Data Mining: Concepts and Techniques 18January 17, 2001January 17, 20013Syntax for interestingness measure specificationn nSyntax for pattern presentation and visualization specificationWe have syntax which allows users to specify the display of discovered patterns in one or more forms display as <result_form> To facilitate interactive viewing at different concept level, the following syntax is defined:Interestingness measures and thresholds can be specified by the user with the statement: with <interest_measure_name> threshold = threshold_valuennExample: with with support threshold= 0.05 0.7confidencethreshold=January 17, 2001Data Mining: Concepts and Techniques19Multilevel_Manipulation ::= roll up on attribute_or_dimension | drill down on attribute_or_dimension | add attribute_or_dimension | drop attribute_or_dimensionConcepts and Techniques January 17, 2001 Data Mining:20Putting it all together: the fullspecification of a DMQL queryuse database AllElectronics_db use hierarchy location_hierarchy for B.address mine characteristics as customerPurchasing analyze count% in relevance to C.age, I.type, I.place_made from customer C, item I, purchases P, items_sold S, works_at W, branch where I.item_ID = S.item_ID and S.trans_ID = P.trans_ID and P. cust_ID = C. cust_ID and P.method_paid = ``AmEx'' and P. empl_ID = W.empl_ID and W.branch_ID = B.branch_ID and B.address = ``Canada" and I.price >= 100 with noise threshold = 0.05 display as tableJanuary 17, 2001 Data Mining: Concepts and Techniques 21 nOther Data Mining Languages & Standardization EffortsAssociation rule language specifications n MSQL (Imielinski & Virmani'99) n MineRule (Meo Psaila and Ceri'96)n Query flocks based on Datalog syntax ( Tsur et al'98) OLEDB for DM (Microsoft'2000) n Based on OLE, OLE DB, OLE DB for OLAP n Integrating DBMS, data warehouse and data mining CRISP-DM (CRoss-Industry Standard Process for Data Mining) n Providing a platform and process structure for effective data mining nnnEmphasizing on deploying data mining technology to solve business problemsData Mining: Concepts and Techniques 22January 17, 2001Chapter 4: Data Mining Primitives, Languages, and System ArchitecturesnDesigning Graphical User Interfaces based on a data mining query languagenData mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query language Architecture of data mining systems SummaryData Mining: Concepts and Techniques 23What tasks should be considered in the design GUIs based on a data mining query language?n n n n n nn nData collection and data mining query composition Presentation of discovered patterns Hierarchy specification and manipulation Manipulation of data mining primitives Interactive multilevel mining Other miscellaneous informationData Mining: Concepts and Techniques 24n nJanuary 17, 2001January 17, 20014Chapter 4: Data Mining Primitives, Languages, and System ArchitecturesnData Mining System ArchitecturesCoupling data mining system with DB/DW system n No coupling—flat file processing, not recommendednnData mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query languageLoose couplingnFetching data from DB/DW Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functionsn nnSemi-tight coupling —enhanced DM performancennn nArchitecture of data mining systems SummaryData Mining: Concepts and Techniques 25Tight coupling —A uniform information processing environmentnDM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc.Data Mining: Concepts and Techniques 26January 17, 2001January 17, 2001Chapter 4: Data Mining Primitives, Languages, and System ArchitecturesnSummaryFive primitives for specification of a data mining task n task -relevant data n kind of knowledge to be mined n background knowledge n interestingness measures n knowledge presentation and visualization techniques to be used for displaying the discovered patterns Data mining query languages n DMQL, MS/OLEDB for DM, etc. Data mining system architecture n No coupling, loose coupling, semi-tight coupling, tight couplingData Mining: Concepts and Techniques 28nData mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query languagenn nn nArchitecture of data mining systems SummaryData Mining: Concepts and Techniques 27nJanuary 17, 2001January 17, 2001Referencesnhttp://www.cs.sfu.ca/~hanE. Baralis and G. Psaila . Designing templates for mining association rules. Journal of Intelligent Information Systems, 9:7-32, 1997. Microsoft Corp., OLEDB for Data Mining, version 1.0, /data/oledb/dm, Aug. 2000. J. Han, Y. Fu, W. Wang, K. Koperski, and O. R. Zaiane, "DMQL: A Data Mining Query Language for Relational Databases", DMKD'96, Montreal, Canada, June 1996. T. Imielinski and A. Virmani. MSQL: A query language for database mining. Data Mining and Knowledge Discovery, 3:373-408, 1999. M. Klemettinen, H. Mannila , P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. CIKM'94, Gaithersburg, Maryland, Nov. 1994. R. Meo, G. Psaila , and S. Ceri. A new SQL-like operator for mining association rules. VLDB'96, pages 122-133, Bombay, India, Sept. 1996. A. Silberschatz and A. Tuzhilin. What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge and Data Engineering, 8:970-974, Dec. 1996. S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database systems: Alternatives and implications. SIGMOD'98, Seattle, Washington, June 1998. D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton, R. Motwani, and S. Nestorov. Query flocks: A generalization of association-rule mining. SIGMOD'98, Seattle, Washington, June 1998.nnnnnnnnThank you !!!29 January 17, 2001 Data Mining: Concepts and Techniques 30January 17, 2001Data Mining: Concepts and Techniques5。
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Biomedical Data Analysis
DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T). Gene: a sequence of hundreds of individual nucleotides arranged in a particular order Humans have around 30,000 genes Tremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genes Semantic integration of heterogeneous, distributed genome databases Current: highly distributed, uncontrolled generation and use of a wide variety of DNA data Data cleaning and data integration methods developed in data mining will help
©2006 Jiawei Han and Micheline Kamber. All rights reserved.
November 28, 2010 Data Mining: Concepts and Techniques 1
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பைடு நூலகம்
Data Mining: Concepts and Techniques
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Data Mining in Retail Industry (2)
Ex. 1. Design and construction of data warehouses based on the benefits of data mining Multidimensional analysis of sales, customers, products, time, and region Ex. 2. Analysis of the effectiveness of sales campaigns Ex. 3. Customer retention: Analysis of customer loyalty Use customer loyalty card information to register sequences of purchases of particular customers Use sequential pattern mining to investigate changes in customer consumption or loyalty Suggest adjustments on the pricing and variety of goods Ex. 4. Purchase recommendation and cross-reference of items
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Data Mining for Telecomm. Industry (1)
A rapidly expanding and highly competitive industry and a great demand for data mining Understand the business involved Identify telecommunication patterns Catch fraudulent activities Make better use of resources Improve the quality of service Multidimensional analysis of telecommunication data Intrinsically multidimensional: calling-time, duration, location of caller, location of callee, type of call, etc.
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Data Mining for Financial Data Analysis
Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high quality Design and construction of data warehouses for multidimensional data analysis and data mining View the debt and revenue changes by month, by region, by sector, and by other factors Access statistical information such as max, min, total, average, trend, etc. Loan payment prediction/consumer credit policy analysis feature selection and attribute relevance ranking Loan payment performance Consumer credit rating
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Data Mining Applications
Data mining is an interdisciplinary field with wide and diverse applications There exist nontrivial gaps between data mining principles and domain-specific applications Some application domains Financial data analysis Retail industry Telecommunication industry Biological data analysis
Data Mining:
Concepts and Techniques
— Chapter 11 —
— Applications
and Trends in Data Mining—
Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign /~hanj
2
Applications and Trends in Data Mining
Data mining applications Data mining system products and research prototypes Additional themes on data mining Social impacts of data mining Trends in data mining Summary
November 28, 2010 Data Mining: Concepts and Techniques 6
Data Mining for Retail Industry
Retail industry: huge amounts of data on sales, customer shopping history, etc. Applications of retail data mining Identify customer buying behaviors Discover customer shopping patterns and trends Improve the quality of customer service Achieve better customer retention and satisfaction Enhance goods consumption ratios Design more effective goods transportation and distribution policies
November 28, 2010 Data Mining: Concepts and Techniques 9
Data Mining for Telecomm. Industry (2)
Fraudulent pattern analysis and the identification of unusual patterns Identify potentially fraudulent users and their atypical usage patterns Detect attempts to gain fraudulent entry to customer accounts Discover unusual patterns which may need special attention Multidimensional association and sequential pattern analysis Find usage patterns for a set of communication services by customer group, by month, etc. Promote the sales of specific services Improve the availability of particular services in a region Use of visualization tools in telecommunication data analysis