Data Mining

Mehmed Kantardzic 지음

Wiley-IEEE Press 2011년 08월 04일 출간

작품소개

책소개

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. Thegoal of this book is toprovide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.If you are an instructor or professor and would like to obtain instructor's materials, please visit http://booksupport.wiley.comIf you are an instructor or professor and would like to obtain a solutions manual, please send an email to: [e-mail]

목차

Preface to the Second Edition xiiiPreface to the First Edition xv1 DATA-MINING CONCEPTS 11.1 Introduction 11.2 Data-Mining Roots 41.3 Data-Mining Process 61.4 Large Data Sets 91.5 Data Warehouses for Data Mining 141.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 171.7 Organization of This Book 211.8 Review Questions and Problems 231.9 References for Further Study 242 PREPARING THE DATA 262.1 Representation of Raw Data 262.2 Characteristics of Raw Data 312.3 Transformation of Raw Data 332.4 Missing Data 362.5 Time-Dependent Data 372.6 Outlier Analysis 412.7 Review Questions and Problems 482.8 References for Further Study 513 DATA REDUCTION 533.1 Dimensions of Large Data Sets 543.2 Feature Reduction 563.3 Relief Algorithm 663.4 Entropy Measure for Ranking Features 683.5 PCA 703.6 Value Reduction 733.7 Feature Discretization: ChiMerge Technique 773.8 Case Reduction 803.9 Review Questions and Problems 833.10 References for Further Study 854 LEARNING FROM DATA 874.1 Learning Machine 894.2 SLT 934.3 Types of Learning Methods 994.4 Common Learning Tasks 1014.5 SVMs 1054.6 kNN: Nearest Neighbor Classifi er 1184.7 Model Selection versus Generalization 1224.8 Model Estimation 1264.9 90% Accuracy: Now What? 1324.10 Review Questions and Problems 1364.11 References for Further Study 1385 STATISTICAL METHODS 1405.1 Statistical Inference 1415.2 Assessing Differences in Data Sets 1435.3 Bayesian Inference 1465.4 Predictive Regression 1495.5 ANOVA 1555.6 Logistic Regression 1575.7 Log-Linear Models 1585.8 LDA 1625.9 Review Questions and Problems 1645.10 References for Further Study 1676 DECISION TREES AND DECISION RULES 1696.1 Decision Trees 1716.2 C4.5 Algorithm: Generating a Decision Tree 1736.3 Unknown Attribute Values 1806.4 Pruni

출판사 서평

“I therefore gladly salute the second editing of this lovely and valuable book. Researchers, students as well as industry professionals can find the reasons, means and practice to make use of essential data mining methodologies to help their interests.”& (Zentralblatt MATH, 2012)&

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저자(글) Mehmed Kantardzic

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MEHMED KANTARDZIC, PhD, is a professor in the Department of Computer Engineering and Computer Science (CECS) in the Speed School of Engineering at the University of Louisville, Director of CECS Graduate Studies, as well as Director of the Data Mining Lab. A member of IEEE, ISCA, and SPIE, Dr. Kantardzic has won awards for several of his papers, has been published in numerous referred journals, and has been an invited presenter at various conferences. He has also been a contributor to numerous books.