數(shù)據(jù)挖掘

內(nèi)容概要

本書對1999年的初版做了重大的改動。雖說核心概念沒有變化，但本書進行了更新使其能反映過去5年里的變化，參考文獻幾乎翻了一番。新版的重要部分包括：30個新的技術(shù)章節(jié)；一個加強了的具有交互式界面的Weka機器學習工作平臺；有關(guān)神經(jīng)網(wǎng)絡(luò)的完整信息，一個有關(guān)貝葉斯網(wǎng)絡(luò)的新節(jié)；等等?！　”緯峁┝藱C器學習概念的完整基礎(chǔ)，此外還針對實際工作中應(yīng)用相關(guān)工具和技術(shù)提出了一些建議，在本書中你將發(fā)現(xiàn)： 　　●成功數(shù)據(jù)挖掘技術(shù)的核心算法，包括歷經(jīng)考驗的真實技術(shù)及前沿的方法。 　　●轉(zhuǎn)換輸入或輸出以改善性能的方法?！　　窨上螺d的Weka軟件??一個用于數(shù)據(jù)挖掘任務(wù)的機器學習算法的集合，包括用于數(shù)據(jù)預(yù)處理、分類、回歸、聚類、關(guān)聯(lián)規(guī)則以及在新的交互式界面上可視化的工具。

作者簡介

Lan H.Witten新西蘭懷卡托大學計算機科學系教授，ACM和新西蘭皇家學會成員。他曾榮獲2004年國際信息處理研究聯(lián)合會頒發(fā)的Namur獎項，這是一個兩年一度的榮譽獎項，用于獎勵那些在信息和通信技術(shù)的社會應(yīng)用方面做出杰現(xiàn)貢獻及具有國際影響的人。他的著作包括《Managing Gi

書籍目錄

ForewordPrefacePart I Machine learning tools and techniques 1. What?s it all about?   1.1 Data mining and machine learning   1.2 Simple examples: the weather problem and others   1.3 Fielded applications   1.4 Machine learning and statistics   1.5 Generalization as search   1.6 Data mining and ethics   1.7 Further reading  2. 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 reading  3. 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 Inferring rudimentary rules   4.2 Statistical modeling   4.3 Divide-and-conquer: constructing decision trees   4.4 Covering algorithms: constructing rules   4.5 Mining association rules   4.6 Linear models   4.7 Instance-based learning   4.8 Clustering   4.9 Further reading  5. Credibility: Evaluating what?s been learned   5.1 Training and testing   5.2 Predicting performance   5.3 Cross-validation   5.4 Other estimates   5.5 Comparing data mining schemes   5.6 Predicting probabilities   5.7 Counting the cost   5.8 Evaluating numeric prediction   5.9 The minimum description length principle   5.10 Applying MDL to clustering   5.11 Further reading  6. Implementations: Real machine learning schemes   6.1 Decision trees   6.2 Classification rules   6.3 Extending linear models   6.4 Instance-based learning   6.5 Numeric prediction   6.6 Clustering   6.7 Bayesian networks  7. Transformations: Engineering the input and output   7.1 Attribute selection   7.2 Discretizing numeric attributes   7.3 Some useful transformations   7.4 Automatic data cleansing   7.5 Combining multiple models   7.6 Using unlabeled data   7.7 Further reading  8. Moving on: Extensions and applications   8.1 Learning from massive datasets   8.2 Incorporating domain knowledge   8.3 Text and Web mining   8.4 Adversarial situations   8.5 Ubiquitous data mining   8.6 Further reading Part II: The Weka machine learning workbench  9. Introduction to Weka   9.1 What?s in Weka?   9.2 How do you use it?   9.3 What else can you do?  9.4 How do you get it?  10. The Explorer   10.1 Getting started   10.2 Exploring the Explorer   10.3 Filtering algorithms   10.4 Learning algorithms   10.5 Meta-learning algorithms   10.6 Clustering algorithms   10.7 Association-rule learners   10.8 Attribute selection  11. The Knowledge Flow interface   11.1 Getting started   11.2 Knowledge Flow components   11.3 Configuring and connecting the components   11.4 Incremental learning  12. The Experimenter   12.1 Getting started   12.2 Simple setup   12.3 Advanced setup   12.4 The Analyze panel  12.5 Distributing processing over several machines  13. The command-line interface   13.1 Getting started   13.2 The structure of Weka   13.3 Command-line options  14. Embedded machine learning …… 15. Writing new learning schemes References Index

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