數(shù)據(jù)挖掘教程

內(nèi)容概要

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(shù)，它是數(shù)據(jù)庫、機器學(xué)習(xí)與統(tǒng)計學(xué)這三個領(lǐng)域的交叉結(jié)合而形成的一門新興技術(shù)。
    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(xué)習(xí)數(shù)據(jù)挖掘的一本好書。全書由四部分構(gòu)成：第一部分是導(dǎo)論，全面介紹了數(shù)據(jù)挖掘的背景信息、相關(guān)概念以及數(shù)據(jù)挖掘所使用的主要技術(shù)；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(guān)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(chǎn)品，包括產(chǎn)品名稱、產(chǎn)品功能、供應(yīng)商、產(chǎn)呂所用技術(shù)、運行平臺及產(chǎn)品狀況。
    全書層分明、要念頭清晰、表達(dá)準(zhǔn)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(xí)和參考文獻(xiàn)為讀者提供了進一步思考相關(guān)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(guān)領(lǐng)域研究人員的參考書。

書籍目錄

Part One Introduction 1 Introduction  1.1 Basic Data Mining Tasks  1.2 Data Mining Versus Knowledge Discovery in Databases  1.3 Data Mining Issues  1.4 Data Mining Metrics  1.5 Social Implications of Data Mining  1.6 Data Mining from a Database Perspective  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 Information Retrieval  2.4 Decison Support Systems  2.5 Dimensional Modeling  2.6 Indexing  2.7 Data Warehousing  2.8 OLAP  2.9 Web Search Engines  2.10 Statistics  2.11 Machine Learning  2.12 Summary  2.13 Exercises  2.14 Bibliographic Notes 3 Data Mining Technques  3.1 Introduction  3.2 A Statistical Perspective on Data Mining  3.3 Similarity Measures  3.4 Decision Trees  3.5 Neural Networks  3.6 Genetic Algorithms  3.7 Exercises  3.8 Bibliographic NotesPart Two Core Topics  4 Classification  4.1 Introduction  4.2 Statistical-Based Algorthms  4.3 Dstance-Baesd Algorithms  4.4 Decision Tree-Based Algouithms  4.5 Neural Network-Based Algorithms  4.6 Rule-Based Algorithms  4.7 Combining Techniques  4.8 Summary  4.9 Exercises  4.10 Bibliographic Notes 5 Chustering  5.1 Introduction  5.2 Similarity and Distance Measures  5.3 Outliers  5.4 Hierarchcal Algorithms  5.5 Partitional Algorithms  5.6 Clustering Large Databases  5.7 Clustering with CATEGOUICAL attrebutes  5.8 Comparises  5.9 Exercise  5.10 Bibliographic Notes 6 Association Rules  6.1 Intrdrction  6.2 Large Itemsets  6.3 Basic Algorithms  6.4 Parallel and Distributed Algorithms  6.5 Comparing Apprlaches  6.6 Incremental Rules  6.7 Advanced Association Rule Techniques  6.8 Measuring the Quality of Rules  6.9 Exercises  6.10 Bibliographic NotesPart Three Advanced Topics  7 Web Mining  7.1 Introduction  7.2 Web Content Mining  7.3 Web Structure Mining  7.4 Web Usage Mining  7.5 Exercises  7.6 Bibliographic Notes 8 Spatial Mining  8.1 Introduction  8.2 Spatial Data Overview   8.3 Spatial Data Mining Primitives  8.4 Generalization and Specialization  8.5 Spatial Rules  8.6 Spatial Classification Algouithm  8.7 Spatial Clustering Algorithms   8.8 Exercises  8.9 Bibliographic Notes 9 Temporal Mining  9.1 Introduction  9.2 Modeling Temporal Events  9.3 Time Series  9.4 Pattern Detcdtion  9.5 Sequences  9.6 Temporal Association Rules  9.7 Exercises  9.8 Bibliographic NotesAPPENDICES A Data Mining Products A.1 Bibliographic NotesB Bibliography Index About the Author

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