Data Mining Concepts and Techniques second edition 数据挖掘概念与技术 第二版 韩家炜 第八章03.PPT
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数据挖掘课件汇总
发现知识的使用
有些人将数据挖掘视为数据库中知识发现的一 个基本步骤，如图
Data
mining: 知识 发现过程的核心过 程.
Task-relevant Data Data Warehouse
Pattern Evaluation
Data Mining
Selection
Data Cleaning Data Integration Databases
天文学
类星体
Web应用
通过分析web访问日志，发现客户的偏好和行为模式，
分析网上市场的效果，改进网站的组织。
Data Mining: Concepts and Techniques
一些具体例子
Data Mining: Concepts and Techniques
一些具体例子
例1：医生给一个病人看病（模式识别的完 整过程）。 测量病人的体温和血压，化验血沉，询问 临床表现； 通过综合分析，抓住主要病症； 医生运用自己的知识，根据主要病症，作 出正确的诊断。
Data Mining: Concepts and Techniques
典型数据挖掘系统的结构
Graphical user interface
Pattern evaluation Data mining engine
Database or data warehouse server
Data cleaning & data integration
发现有用特征, 维和变量约简.转化成适合挖掘的形式 摘要, 分类, regression(回归）, 关联, 聚类.
数据挖掘功能选择
数据预处理 数据与统计
Data transformation and data discretization Normalization Concept hierarchy generation
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Chapter 3: Data Preprocessing
Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing
e.g., Occupation=“ ” (missing data)
noisy: containing noise, errors, or outliers
e.g., Salary=“−10” (an error)
inconsistent: containing discrepancies in codes or names, e.g.,
Data migration and integration Data migration tools: allow transformations to be specified ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface
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How to Handle Noisy Data?
Binning first sort data and partition into (equal-frequency) bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.
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Chapter 3: Data Preprocessing
Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing
e.g., Occupation=“ ” (missing data)
noisy: containing noise, errors, or outliers
e.g., Salary=“−10” (an error)
inconsistent: containing discrepancies in codes or names, e.g.,
Data migration and integration Data migration tools: allow transformations to be specified ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface
9
How to Handle Noisy Data?
Binning first sort data and partition into (equal-frequency) bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.
数据采集和营销工具(英文版)
Motivations for DM
Abundance of business and industry data
Competitive focus - Ke, powerful computing engines
Strong theoretical/mathematical foundations
1. Decision Trees and Fraud Detection
2. Association Rules and Market Basket Analysis 3. Clustering and Customer Segmentation
3. Trends in technology
1. Knowledge Discovery Support Environment 2. Tools, Languages and Systems
Provide a systematization to the many many concepts around this area, according the following lines
the process the methods applied to paradigmatic cases the support environment the research challenges
1970s:
Relational data model, relational DBMS implementation.
1980s:
RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.).
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。
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。
韩家炜数据挖掘讲座PPT03
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Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation From data warehousing to data mining
and stored in warehouses for direct query and analysis
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Data Warehouse vs. Operational DBMS
OLTP (on-line transaction processing)
the organization’s operational database Support information processing by providing a solid platform of
consolidated, historical data for analysis.
“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s
decision-making process.”—W. H. Inmon
Data Mining：Concepts and Techniques
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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?
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?
第二十一章 形式化建模与验证
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21.1 净室策略
净室方法使用第2章所介绍的增量过程模型的专 业版。一个“软件增量的流水线”[Lin94b]由若干小 的、独立的软件团队开发。每当一个软件增量通过认 证，它就被集成到整体系统中。因此，系统的功能随 时间增加。
增量1 RG
BSS
FD
CV
TP
CG
CI
SUT
C
增量2 SE RG BSS FD CV TP BSS FD CV TP 净室过程模型
清晰盒包含了对状态盒的过程设计。
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Data Mining: Concepts and Techniques
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CB1.1.1.1 BB1.1.1 SB1.1.1 CB1.1.1.2
BB1.1 BB1
BB1.2
BB1.1.2 BB1.1.3
CB1.1.1.3
BB1.n
盒结构求精
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Cont:[y2≤x]
y=y+1
21.4 净室测试
传统的测试方法导出一组测试用例，以发现设计和编码
错误；净室测试的目的是通过证明用例的统计样本的成 功运行来确认软件需求。
2014年12月11日星期四
得多，这种严格需要更多的工作量，单从一致性和完整性 的提高方面得到的好处在很多类型的应用中得到证明。
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21.7
形式化规格说明语言
形式化规格说明语言通常由3个主要成分构成：
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Sup 3 5 4 3 3 2 1 1
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GSP: Generating Length-2 Candidates
51 length-2 Candidates
<a> <b> <c> <d> <e> <f>
<a> <aa> <ba> <ca> <da> <ea> <fa>
©2006 Jiawei Han and Micheline Kamber. All rights reserved.
November 28, 2010 Data Mining: Concepts and Techniques
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November 28, 2010
Data Mining: Concepts and Techniques
November 28, 2010 Data Mining: Concepts and Techniques
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The Apriori Property of Sequential Patterns
A basic property: Apriori (Agrawal & Sirkant’94) If a sequence S is not frequent Then none of the super-sequences of S is frequent E.g, <hb> is infrequent
A
sequence : < (ef) (ab)
(df) c b >
A sequence database
SID 10 20 30 40 sequence <a(abc)(ac)d(cf)> <(ad)c(bc)(ae)> <(ef)(ab)(df)cb> <eg(af)cbc>
An element may contain a set of items. Items within an element are unordered and we list them alphabetically.
<a(bc)dc> is a subsequence of <a(abc)(ac)d(cf)>
Given support threshold min_sup =2, <(ab)c> is a
sequential pattern
November 28, 2010 Data Mining: Concepts and Techniques
<b> <ab> <bb> <cb> <db> <eb> <fb>
<c> <ac> <bc> <cc> <dc> <ec> <fc>
<d> <ad> <bd> <cd> <dd> <ed> <fd>
<e> <ae> <be> <ce> <de> <ee> <fe>
<f> <af> <bf> <cf> <df> <ef> <ff>
November 28, 2010 Data Mining: Concepts and Techniques
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Finding Length-1 Sequential Patterns
Examine GSP using an example Initial candidates: all singleton sequences <a>, <b>, <c>, <d>, <e>, <f>, <g>, <h> Scan database once, count support for candidates min_sup =2
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Challenges on Sequential Pattern Mining
A huge number of possible sequential patterns are hidden in databases A mining algorithm should find the complete set of patterns, when possible, satisfying the minimum support (frequency) threshold be highly efficient, scalable, involving only a small number of database scans be able to incorporate various kinds of user-specific constraints
November 28, 2010 Data Mining: Concepts and Techniques
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What Is Sequential Pattern Mining?
Given a set of sequences, find the complete set of frequent subsequences
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Data Mining: Concepts and Techniques
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Sequential Pattern Mining Algorithms
Concept introduction and an initial Apriori-like algorithm Agrawal & Srikant. Mining sequential patterns, ICDE’95 Apriori-based method: GSP (Generalized Sequential Patterns: Srikant & Agrawal @ EDBT’96) Pattern-growth methods: FreeSpan & PrefixSpan (Han et al.@KDD’00; Pei, et al.@ICDE’01) Vertical format-based mining: SPADE (Zaki@Machine Leanining’00) Constraint-based sequential pattern mining (SPIRIT: Garofalakis, Rastogi, Shim@VLDB’99; Pei, Han, Wang @ CIKM’02) Mining closed sequential patterns: CloSpan (Yan, Han & Afshar @SDM’03)
<a> <a> <b> <c> <d> <e> <f>
<b> <(ab)>
<c> <(ac)> <(bc)>
<d> <(ad)> <(bd)> <(cd)>
<e> <(ae)> <(be)> <(ce)> <(de)>
<f> <(af)> <(bf)> <(cf)> <(df)> <(ef)>
Without Apriori property, 8*8+8*7/2=92 candidates
Seq. ID 10 20 30 40 50
November 28, 2010
Sequence <(bd)cb(ac)> <(bf)(ce)b(fg)> <(ah)(bf)abf> <(be)(ce)d> <a(bd)bcb(ade)>
Cand <a> <b> <c> <d> <e> <f> <g> <h>
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Apriori prunes 44.57% candidates Data Mining: Concepts and Techniques
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The GSP Mining Process
Cand. cannot pass sup. threshold
5th scan: 1 cand. 1 length-5 seq. pat.
Seq. ID 10 20 30 40 50 Sequence <(bd)cb(ac)> <(bf)(ce)b(fg)> <(ah)(bf)abf> <(be)(ce)d> <a(bd)bcb(ade)>
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so do <hab> and <(ah)b>
Given support threshold min_sup =2
Data Mining:
Concepts and Techniques
— Chapter 8 —
8.3 Mining sequence patterns in transactional databases Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaignning: Concepts and Techniques