Data Mining. Concepts, Models, Methods, and Algorithms. 2nd Edition

Transcription

1 Brochure More information from Data Mining. Concepts, Models, Methods, and Algorithms. 2nd Edition Description: 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. The goal of this book is to provide 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 company website If you are an instructor or professor and would like to obtain a solutions manual, please send an to: company website Contents: Preface to the Second Edition xiii Preface to the First Edition xv 1 DATA-MINING CONCEPTS Introduction Data-Mining Roots Data-Mining Process Large Data Sets Data Warehouses for Data Mining Business Aspects of Data Mining: Why a Data-Mining Project Fails Organization of This Book Review Questions and Problems References for Further Study 24 2 PREPARING THE DATA Representation of Raw Data Characteristics of Raw Data Transformation of Raw Data Missing Data Time-Dependent Data Outlier Analysis Review Questions and Problems References for Further Study 51

2 3 DATA REDUCTION Dimensions of Large Data Sets Feature Reduction Relief Algorithm Entropy Measure for Ranking Features PCA Value Reduction Feature Discretization: ChiMerge Technique Case Reduction Review Questions and Problems References for Further Study 85 4 LEARNING FROM DATA Learning Machine SLT Types of Learning Methods Common Learning Tasks SVMs knn: Nearest Neighbor Classifi er Model Selection versus Generalization Model Estimation % Accuracy: Now What? Review Questions and Problems References for Further Study STATISTICAL METHODS Statistical Inference Assessing Differences in Data Sets Bayesian Inference Predictive Regression ANOVA Logistic Regression Log-Linear Models LDA 162

3 5.9 Review Questions and Problems References for Further Study DECISION TREES AND DECISION RULES Decision Trees C4.5 Algorithm: Generating a Decision Tree Unknown Attribute Values Pruning Decision Trees C4.5 Algorithm: Generating Decision Rules CART Algorithm & Gini Index Limitations of Decision Trees and Decision Rules Review Questions and Problems References for Further Study ARTIFICIAL NEURAL NETWORKS Model of an Artifi cial Neuron Architectures of ANNs Learning Process Learning Tasks Using ANNs Multilayer Perceptrons (MLPs) Competitive Networks and Competitive Learning SOMs Review Questions and Problems References for Further Study ENSEMBLE LEARNING Ensemble-Learning Methodologies Combination Schemes for Multiple Learners Bagging and Boosting AdaBoost Review Questions and Problems References for Further Study CLUSTER ANALYSIS Clustering Concepts 250

4 9.2 Similarity Measures Agglomerative Hierarchical Clustering Partitional Clustering Incremental Clustering DBSCAN Algorithm BIRCH Algorithm Clustering Validation Review Questions and Problems References for Further Study ASSOCIATION RULES Market-Basket Analysis Algorithm Apriori From Frequent Itemsets to Association Rules Improving the Effi ciency of the Apriori Algorithm FP Growth Method Associative-Classifi cation Method Multidimensional Association Rules Mining Review Questions and Problems References for Further Study WEB MINING AND TEXT MINING Web Mining Web Content, Structure, and Usage Mining HITS and LOGSOM Algorithms Mining Path Traversal Patterns PageRank Algorithm Text Mining Latent Semantic Analysis (LSA) Review Questions and Problems References for Further Study ADVANCES IN DATA MINING Graph Mining Temporal Data Mining 343

5 12.3 Spatial Data Mining (SDM) Distributed Data Mining (DDM) Correlation Does Not Imply Causality Privacy, Security, and Legal Aspects of Data Mining Review Questions and Problems References for Further Study GENETIC ALGORITHMS Fundamentals of GAs Optimization Using GAs A Simple Illustration of a GA Schemata TSP Machine Learning Using GAs GAs for Clustering Review Questions and Problems References for Further Study FUZZY SETS AND FUZZY LOGIC Fuzzy Sets Fuzzy-Set Operations Extension Principle and Fuzzy Relations Fuzzy Logic and Fuzzy Inference Systems Multifactorial Evaluation Extracting Fuzzy Models from Data Data Mining and Fuzzy Sets Review Questions and Problems References for Further Study VISUALIZATION METHODS Perception and Visualization Scientifi c Visualization and Information Visualization Parallel Coordinates 455

6 15.4 Radial Visualization Visualization Using Self-Organizing Maps (SOMs) Visualization Systems for Data Mining Review Questions and Problems References for Further Study 468 Appendix A 470 A.1 Data-Mining Journals 470 A.2 Data-Mining Conferences 473 A.3 Data-Mining Forums/Blogs 477 A.4 Data Sets 478 A.5 Comercially and Publicly Available Tools 480 A.6 Web Site Links 489 Appendix B: Data-Mining Applications 496 B.1 Data Mining for Financial Data Analysis 496 B.2 Data Mining for the Telecomunications Industry 499 B.3 Data Mining for the Retail Industry 501 B.4 Data Mining in Health Care and Biomedical Research 503 B.5 Data Mining in Science and Engineering 506 B.6 Pitfalls of Data Mining 509 Bibliography 510 Index 529 Ordering: Order Online - Order by Fax - using the form below Order by Post - print the order form below and send to Research and Markets, Guinness Centre, Taylors Lane, Dublin 8, Ireland.

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