數(shù)據(jù)挖掘

內容概要

這是一本將數(shù)據(jù)挖掘算法和數(shù)據(jù)挖掘實踐完美結合起來的優(yōu)秀教材。作者以其豐富的經驗，對數(shù)據(jù)挖掘的概念和數(shù)據(jù)挖掘所有的技術（特別是機器學習）進行了深入淺出的介紹，并對應用機器學習工具進行數(shù)據(jù)挖掘給出了良好的建議。數(shù)據(jù)挖掘中的各個關鍵要素也事例融合在眾多實例中加以介紹。
    本書還介紹了Weka這種基于Java的軟件系統(tǒng)。該軟件系統(tǒng)可以用來分析數(shù)據(jù)集，找到適用的模式，進行正確的分析，也可以用來開發(fā)自己的機器學方案。
本書的主要特點：     
    解釋數(shù)據(jù)挖掘算法的原理。
    通過實例幫助讀者根據(jù)實際情況選擇合適的算法，并比較和評估不同方法得出的結果。
    介紹提高性能的技術，包括數(shù)據(jù)處理以及組合不同方法得到的輸出。
    提供了本書所有的Weka軟件和附加學習材料，可以從http://www.mkp.com/datamining上下載這些資料。

作者簡介

Lan H.Witten，新西蘭懷卡托大學計算機科學系教授。他是ACM和新西蘭皇家學會的成員，并參加了英國、美國、加拿大和新西蘭的專業(yè)計算、信息檢索、工程等協(xié)會。他著有多部著作，是多家技術雜志的作者，發(fā)表過大量論文。

書籍目錄

ForewordPreface1 What's it all about? 1.1 Data mining and machine learning 1.2 Simple examples:The weather problem and others 1.3 Fielded application 1.4 Machine learning and statistics 1.5 Generalization as search 1.6 Data mining and ethics 1.7 Further reading2 Input:Concepts,instances,attributes 2.1 What's a concept? 2.2 What's in an example? 2.3 What's in an attribute? 2.4 Preparing the input 2.5 Further reading3 Output:Knowledge representation 3.1 Decision tables 3.2 Decision trees 3.3 Classification rules 3.4 Association rules 3.5 Rules with exceptions 3.6 Rules involving relations 3.7 Trees for numeric prediction 3.8 Instance-based representation 3.9 Clusters 3.10 Further reading 4 Algorithms:The basic methods 4.1 Infereing rudimentary rules 4.2 Statistical modeling 4.3 Divide and conuquer:Constructing decision trees 4.4 Covering algorithms:Construsting rules 4.5 Mining association rules 4.6 Linear models 4.7 Instance-based learning 4.8 Further reading5 Credibility:Evaluation what's been learnde 5.1 Training and testing 5.2 predicting per formance 5.3 Cross-vaidation 5.4 Other estimates 5.5 Comparing data mining schems 5.6 Predicting Probabilities 5.7 Counting the cost 5.8 Evaluating numer ic prediction 5.9 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading6 Implemententation:Real machine learning schemes 6.1 Decision tress 6.2 Classification rules 6.3 Extending linear classification:Support vector machines 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering7 Moving on:Engineering the input and output 7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Automtic data cleansing 7.4 Combining multiple models 7.5 Further reading8 Nuts and bolts:Machine learning algorithms in Java 8.1 Getting started 8.2 Javadoc and the class library 8.3 Processing dataset using the machine learning programs 8.4 Embedded machine learning 8.5 Writing new learning schemes9 Looking forward 9.1 learning from massive datasets 9.2 Visualizing machine learning 9.3 Incorporation domain knowlgdge 9.4 Text mining 9.5 Mining the World Wide Web 9.6 Further readingReferencesIndexAbout the authors

媒體關注與評論

書評本書是綜合運用數(shù)據(jù)挖掘、數(shù)據(jù)分析、信息理論通訊機器學習技術的里程碑。  		  		  		  

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　  其它版本請見：《經典原版書庫·數(shù)據(jù)挖掘：實用機器學習技術（英文版）（第2版）（新版）》

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