數(shù)據(jù)挖掘基礎(chǔ)教程

內(nèi)容概要

數(shù)據(jù)挖掘就是發(fā)現(xiàn)數(shù)據(jù)模型，以助于解釋當前行為或預測將來的可能結(jié)果。本書介紹了數(shù)據(jù)挖掘的基本過程，解釋了如何將數(shù)據(jù)挖掘應用于解決實際問題，從而使你能將數(shù)據(jù)挖掘技術(shù)應用于自己的實際工作中去。本書講述了數(shù)據(jù)挖掘和知識發(fā)現(xiàn)的各方面內(nèi)容，并著重介紹了數(shù)據(jù)挖掘模型的建立與測試，以及數(shù)據(jù)挖掘結(jié)果的解釋與驗證等內(nèi)容。為了使讀者更好地理解數(shù)據(jù)挖掘過程，在本書配套光盤中提供了一個基于Microsoft Excel的數(shù)據(jù)挖掘工具，讀者可以親身體驗數(shù)據(jù)挖掘模型的建立與測試。 
　　本書可作為相關(guān)專業(yè)的本科生教材，對需要理解數(shù)據(jù)挖掘和智能系統(tǒng)的專業(yè)人員也是很好的參考書。

書籍目錄

Part I Data Mining Fundamentals chapter 1 Data Mining:A First View      1.1 Data Mining:A Definition     1.2 What Can Computers Learn?         Three concept Views         Supervised Learing          Supervised Learing:A Decision for Tree Example          Unsupervised Clustering     1.3 Is Data Mining Appropriate for My Problem?         Data Mining or Data Query?         Data Mining vs.Data Query:An Example      1.4 Expert Systems or Data Mining?     1.5 A Simple Data Mining Process Model         Assembling the Data         The Data Warehouse          Relational Databases and Flat Files         Mining the Data         Interpreting the Results         Result application     1.6 Why Not Simple Search?     1.7 Data Mining Applications         Example Applications         Customer Intrinsic Value      1.8 chapter Summary     1.9 Key Terms     1.10 ExercisesChapter 2 Data Mining:A closer Look     2.1 Data Mining Strategies         classification         Estimation         Prediction         Unsupervised clustering          Market Basket Ananlysis     2.2 Supervised Data Mining Database         the Credit Card Promotion Database          Production Rules         Neural Networks         Statistical Regression     2.3 Association Rules     2.4 Clustering techniques     2.5 Evaluating Performance         evaluating supervised Learner Models          Two Class Error Analysis         Evaluating Numeric Output          Unsupervised Moedl Evaluation     2.6 chapter Summary      2.7 Key Terms     2.8 ExercisesChapter 3 Basic Data Mining TechniquesChapter 4 An Excel-Based Data Mining ToolPart 2 Advanced Data Mining Techniques  Chapter 8 Nerual Networks  Chapter 9 Building Nerual Networks with IDA  Chapter 10 Staticstical Techniques  Chapter 11 Specialized TechniquesPart 4:Intelligent Systems  Chapter 12 Rule-Based Systems  Chapter 13 Managing Uncertainty in Rule-Based System  Chapter 14 Intelligent Agents  Appendixes  Appendix A The iDASoftware  Appendix B Datasets for Data Mining   Appendix C Decision Tree Atrribute Selection  Appendix D Statistics for Performance Evaluation  Appendix E Excel Pivot Tables:Office 97  Bibliography  Index

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