數(shù)據(jù)挖掘教程

定　價：￥34.80

作　者：	（美）Margaret H.Dunham著
出版社：	清華大學出版社
叢編項：	計算機科學與技術學科研究生系列教材
標　簽：	數(shù)據(jù)庫存儲與管理

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ISBN：	9787302073833	出版時間：	2003-01-01	包裝：	簡裝本
開本：	23cm	頁數(shù)：	315	字數(shù)：

內容簡介

　　本書全面系統(tǒng)地介紹了各種數(shù)據(jù)挖掘的基本概念、方法和算法，是系統(tǒng)學習數(shù)據(jù)挖掘的一本好書。全書由四部分構成：第一部分是導論，全面介紹了數(shù)據(jù)挖掘的背景信息、相關概念以及數(shù)據(jù)挖掘所使用的主要技術；第二部分？鞘萃誥虻暮誦乃惴?，系统舍摎柝描孰h擻糜詵擲唷⒕劾嗪凸亓嬖虻某Ｓ盟惴ǎ壞諶糠質鞘萃誥虻母嘸犢翁?，主要轩橂h薟eb挖掘、空間數(shù)據(jù)挖掘、時序數(shù)據(jù)和序列數(shù)據(jù)挖掘；第四部分是附錄，介紹了目前市場上流行的一些數(shù)據(jù)挖掘工具產品，包括產品名稱、產品功能、供應商、產呂所用技術、運行平臺及產品狀況。數(shù)據(jù)挖掘是近年來伴隨著數(shù)據(jù)庫系統(tǒng)的大量建立和萬維網(wǎng)的廣泛使用而發(fā)展起來的一門技術，它是數(shù)據(jù)庫、機器學習與統(tǒng)計學這三個領域的交叉結合而形成的一門新興技術。本書全面系統(tǒng)地介紹了各種數(shù)據(jù)挖掘的基本概念、方法和算法，是系統(tǒng)學習數(shù)據(jù)挖掘的一本好書。全書由四部分構成：第一部分是導論，全面介紹了數(shù)據(jù)挖掘的背景信息、相關概念以及數(shù)據(jù)挖掘所使用的主要技術；第二部分是數(shù)據(jù)挖掘的核心算法，系統(tǒng)深入地描述了用于分類、聚類和關聯(lián)規(guī)則的常用算法；第三部分是數(shù)據(jù)挖掘的高級課題，主要敘述了Web挖掘、空間數(shù)據(jù)挖掘、時序數(shù)據(jù)和序列數(shù)據(jù)挖掘；第四部分是附錄，介紹了目前市場上流行的一些數(shù)據(jù)挖掘工具產品，包括產品名稱、產品功能、供應商、產呂所用技術、運行平臺及產品狀況。全書層分明、要念頭清晰、表達準確、體系完整。書中對每種算法不僅進行了詳盡的解釋，還給出了算例及偽代碼。每章后的練習和參考文獻為讀者提供了進一步思考相關問題的線索。本書適宜作為計算機專業(yè)研究生、高年級本科生教材，也可作為相關領域研究人員的參考書。

作者簡介

暫缺《數(shù)據(jù)挖掘教程》作者簡介

圖書目錄

Part One Introduction
Introduction
1.1  Basic Dam Mining Tasks
1.1.1  C1aSSi6cation
1.1.2  Regression
1.1.3  Time Series Analysis
1.1.4  Prediction
1.1.5  Clustering
1.1.6  Summarization
1.1.7  Association Rules
1.1.8  Sequence Discovery
1.2  Data Mining Versus Knowledge Discovery in Databases
1.2.1  The Development Of Data Mining
1.3  Dam Mining Issues
1.4  Data Mining Metrics
1.5  Social Implications Of Data Mining
1.6  Dam Mining from a Database Perspec6ve
1.7  The Future
1.8  Exercises
1.9  Bibliographic Notes
2  Related Concepts
2.1  Database／OLTP Systems
2.2  Fuzzy Sets and Fuzzy Logic
2.3  1nformiton Retrieval
2.4  Decision Support Systems
2.5  Dimensional MOdenn9
2.5.1  Multidimensional Schemas
2.5.2  1ndexing
2.6  Data Warehousing
2.7  OLAP.
2.8  Web Search Engines
2.9  Statistics
2.10  Machine Leaning
2.11  Pattern Matching
2.12  Summary
2.13  Exercises
2.14  Bibliographic Notes
3  Data Mining Techniques
3.1  Introduction
3.2  A Statistical Perspective on Data Mining
3.2.1  Point Estimation
3.2.2  Models Based on Summarization
3.2.3  Bayes Theorem
3.2.4  Hypothesis Testing
3.2.5  Regression and Correlation
3.3  Similarity Measures
3.4  Decision Trees
3.5  Neural Networks
3.5.1  Activation Functions
3.6  Genetic Algorithms
3.7  Exercises
3.8  Bibliographic Notes
Part Two  Core Topics
4 Classification
4.1  Introduction
4.1.1  Issues in Classification
4.2  Statistical-Based Algorithms
4.2.1  Regression
4.2.2  Bayesian Classification
4.3  Distance-Based Algorithms
4.3.1  Simple Approach
4.3.2  K Nearest Neighbors
4.4  Decision Tree-Based Algorithms
4.4.1  ID3
4.4.2  C4.5 and C5.0
4.4.3  CART
4.4.4  Scalable DT Techniques
4.5  Neural Network—Based Algorithms
4.5.1  Propagation
4.5.2  NN Supervised Learning
4.5.3  Radial Basis Function Networks
4.5.4  Perceptrons
4.6  Rule-Based Algorithms
4.6.1  Generating Rules from a DT
4.6.2  Generating Rules from a Neural Net
4.6.3  Generating Rules Without a DT or NN
4.7  Combining Techniques
4.8  Summary
4.9  Exercises
4.10  Bibliographic Notes
5  Clustering
5.1  Introduction
5.2  Similarity and Distance Measures
5.3  Outliers
5.4  Hierarchical Algorithms
5.4.1  Agglomerative Algorithms
5.4.2  Divisive Clustering
5.5  Partitional Algorithms
5.5.1  Minimum Spanning Tree
5.5.2  Squared Error Clustering Algorithm
5.5.3  K-Means Clustering
5.5.4  Nearest Neighbor Algorithm
5.5.5  PAM Algorithm
5.5.6  Bond Energy Algorithm
5.5.7  Clustering with Genetic Algorithms
5.5.8  Clustering with Neural Networks
5.6  Clustering Large Databases
5.6.1  BIRCH
5.6.2  DBSCAN
5.6.3  CURE Algorithm
5.7  Clustering with Categorical Attributes
5.8  Comparison
5.9  Exercises
5.10  Bibliographic Notes
6  Association Rules
6.1  Introduction
6.2  Large Itemsets
6.3  Basic Algorithms
6.3.1  Apriori Algorithm
6.3.2  Sampling Algorithm
6.3.3  Partitioning
6.4  Parallel and Distributed Algorithms
6.4.1  Data Parallelism
6.4.2  Task Parallelism
6.5  Comparing Approaches
6.6  Incremental Rules
6.7  Advanced Association Rule Techniques
6.7.1  Generalized Association Rules
6.7.2  Multiple-Level Association Rules
6.7.3  Quantitative Association Rules
6.7.4  Using Multiple Minimum Supports
6.7.5  Correlation Rules
6.8  Measuring the Quality of Rules
6.9  Exercises
6.10 Bibliographic Notes
Part Three  Advanced Topics
7  Web Mining
7.1  Introduction
7.2  Web Content Mining
7.2.1  Crawlers
7.2.2  Harvest System
7.2.3  Virtual Web View
7.2.4  Personalization
7.3  Web Structure Mining
7.3.1  Page Rank
7.3,2  Clever
7.4  Web Usage Mining
7.4,1  Preprocessing
7.4.2  Data Structures
7,4.3  Pattern Discovery
7.4.4  Pattern Analysis
7.5  Exercises
7.6  Bibliographic Notes
8  Spatial Mining
8.1  Introduction
8.2  Spatial Data Overview
8.2.1  Spatial Queries
8.2.2  Spatial Data Structures
8.2.3  Thematic Maps
8.2.4  Image Databases
8.3  Spatial Data Mining Primitives
8.4  Generalization and Specialization
8.4.1  Progressive Refinement
8.4.2  Generalization
8.4.3  Nearest Neighbor
8.4.4  STING
8.5  Spatial Rules
8.5.1  Spatial Association Rules
8.6  Spatial Classification Algorithm
8.6.1  ID3 Extension
8,6.2  Spatial Decision Tree
8.7  Spatial Clustering Algorithms
8.7,1  CLARANS Extensions
8.7.2  SD(CLARANS)
8.7.3  DBCLASD
8.7.4  BANG
8.7.5  Wave Cluster
8.7.6  Approximation
8.8  Exercises
8.9  Bibliographic Notes
9  Temporal Mining
9.1  Introduction
9.2  Modeling Temporal Events
9.3  Time Series
9.3.1  Time Series Analysis
9.3.2  Trend Analysis
9.3.3  Transformation
9.3.4  Similarity
9.3.5  Prediction
9.4  Pattern Detection
9.4.1  String Matching
9.5  Sequences
9.5.1 AprioriAll
9.5.2  SPADE
9.5.3  Generalization
9.5.4  Feature Extraction
9.6  Temporal Association Rules
9.6.1  Intertransaction Rules
9.6.2  Episode Rules
9.6.3  Trend Dependencies
9.6.4  Sequence Association Rules
9.6.5  Calendric Association Rules
9.7  Exercises
9.8  Bibliographic Notes
APPENDICES
A  Data Mining Products
A.1 Bibliographic Notes
B  Bibliography
Index
About the Author