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Data mining : concepts, models, methods, and algorithms / / Mehmed Kantardzic
Data mining : concepts, models, methods, and algorithms / / Mehmed Kantardzic
Autore Kantardzic Mehmed
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley, , c2011
Descrizione fisica 1 online resource (554 p.)
Disciplina 005.741
006.3/12
Soggetto topico Data mining
ISBN 1-283-23974-4
9786613239747
1-118-02913-5
1-118-02912-7
1-118-02914-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface to the Second Edition xiii -- Preface to the First Edition xv -- 1 DATA-MINING CONCEPTS 1 -- 1.1 Introduction 1 -- 1.2 Data-Mining Roots 4 -- 1.3 Data-Mining Process 6 -- 1.4 Large Data Sets 9 -- 1.5 Data Warehouses for Data Mining 14 -- 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 -- 1.7 Organization of This Book 21 -- 1.8 Review Questions and Problems 23 -- 1.9 References for Further Study 24 -- 2 PREPARING THE DATA 26 -- 2.1 Representation of Raw Data 26 -- 2.2 Characteristics of Raw Data 31 -- 2.3 Transformation of Raw Data 33 -- 2.4 Missing Data 36 -- 2.5 Time-Dependent Data 37 -- 2.6 Outlier Analysis 41 -- 2.7 Review Questions and Problems 48 -- 2.8 References for Further Study 51 -- 3 DATA REDUCTION 53 -- 3.1 Dimensions of Large Data Sets 54 -- 3.2 Feature Reduction 56 -- 3.3 Relief Algorithm 66 -- 3.4 Entropy Measure for Ranking Features 68 -- 3.5 PCA 70 -- 3.6 Value Reduction 73 -- 3.7 Feature Discretization: ChiMerge Technique 77 -- 3.8 Case Reduction 80 -- 3.9 Review Questions and Problems 83 -- 3.10 References for Further Study 85 -- 4 LEARNING FROM DATA 87 -- 4.1 Learning Machine 89 -- 4.2 SLT 93 -- 4.3 Types of Learning Methods 99 -- 4.4 Common Learning Tasks 101 -- 4.5 SVMs 105 -- 4.6 kNN: Nearest Neighbor Classifi er 118 -- 4.7 Model Selection versus Generalization 122 -- 4.8 Model Estimation 126 -- 4.9 90% Accuracy: Now What? 132 -- 4.10 Review Questions and Problems 136 -- 4.11 References for Further Study 138 -- 5 STATISTICAL METHODS 140 -- 5.1 Statistical Inference 141 -- 5.2 Assessing Differences in Data Sets 143 -- 5.3 Bayesian Inference 146 -- 5.4 Predictive Regression 149 -- 5.5 ANOVA 155 -- 5.6 Logistic Regression 157 -- 5.7 Log-Linear Models 158 -- 5.8 LDA 162 -- 5.9 Review Questions and Problems 164 -- 5.10 References for Further Study 167 -- 6 DECISION TREES AND DECISION RULES 169 -- 6.1 Decision Trees 171 -- 6.2 C4.5 Algorithm: Generating a Decision Tree 173 -- 6.3 Unknown Attribute Values 180 -- 6.4 Pruning Decision Trees 184.
6.5 C4.5 Algorithm: Generating Decision Rules 185 -- 6.6 CART Algorithm & Gini Index 189 -- 6.7 Limitations of Decision Trees and Decision Rules 192 -- 6.8 Review Questions and Problems 194 -- 6.9 References for Further Study 198 -- 7 ARTIFICIAL NEURAL NETWORKS 199 -- 7.1 Model of an Artifi cial Neuron 201 -- 7.2 Architectures of ANNs 205 -- 7.3 Learning Process 207 -- 7.4 Learning Tasks Using ANNs 210 -- 7.5 Multilayer Perceptrons (MLPs) 213 -- 7.6 Competitive Networks and Competitive Learning 221 -- 7.7 SOMs 225 -- 7.8 Review Questions and Problems 231 -- 7.9 References for Further Study 233 -- 8 ENSEMBLE LEARNING 235 -- 8.1 Ensemble-Learning Methodologies 236 -- 8.2 Combination Schemes for Multiple Learners 240 -- 8.3 Bagging and Boosting 241 -- 8.4 AdaBoost 243 -- 8.5 Review Questions and Problems 245 -- 8.6 References for Further Study 247 -- 9 CLUSTER ANALYSIS 249 -- 9.1 Clustering Concepts 250 -- 9.2 Similarity Measures 253 -- 9.3 Agglomerative Hierarchical Clustering 259 -- 9.4 Partitional Clustering 263 -- 9.5 Incremental Clustering 266 -- 9.6 DBSCAN Algorithm 270 -- 9.7 BIRCH Algorithm 272 -- 9.8 Clustering Validation 275 -- 9.9 Review Questions and Problems 275 -- 9.10 References for Further Study 279 -- 10 ASSOCIATION RULES 280 -- 10.1 Market-Basket Analysis 281 -- 10.2 Algorithm Apriori 283 -- 10.3 From Frequent Itemsets to Association Rules 285 -- 10.4 Improving the Effi ciency of the Apriori Algorithm 286 -- 10.5 FP Growth Method 288 -- 10.6 Associative-Classifi cation Method 290 -- 10.7 Multidimensional Association-Rules Mining 293 -- 10.8 Review Questions and Problems 295 -- 10.9 References for Further Study 298 -- 11 WEB MINING AND TEXT MINING 300 -- 11.1 Web Mining 300 -- 11.2 Web Content, Structure, and Usage Mining 302 -- 11.3 HITS and LOGSOM Algorithms 305 -- 11.4 Mining Path-Traversal Patterns 310 -- 11.5 PageRank Algorithm 313 -- 11.6 Text Mining 316 -- 11.7 Latent Semantic Analysis (LSA) 320 -- 11.8 Review Questions and Problems 324 -- 11.9 References for Further Study 326.
12 ADVANCES IN DATA MINING 328 -- 12.1 Graph Mining 329 -- 12.2 Temporal Data Mining 343 -- 12.3 Spatial Data Mining (SDM) 357 -- 12.4 Distributed Data Mining (DDM) 360 -- 12.5 Correlation Does Not Imply Causality 369 -- 12.6 Privacy, Security, and Legal Aspects of Data Mining 376 -- 12.7 Review Questions and Problems 381 -- 12.8 References for Further Study 382 -- 13 GENETIC ALGORITHMS 385 -- 13.1 Fundamentals of GAs 386 -- 13.2 Optimization Using GAs 388 -- 13.3 A Simple Illustration of a GA 394 -- 13.4 Schemata 399 -- 13.5 TSP 402 -- 13.6 Machine Learning Using GAs 404 -- 13.7 GAs for Clustering 409 -- 13.8 Review Questions and Problems 411 -- 13.9 References for Further Study 413 -- 14 FUZZY SETS AND FUZZY LOGIC 414 -- 14.1 Fuzzy Sets 415 -- 14.2 Fuzzy-Set Operations 420 -- 14.3 Extension Principle and Fuzzy Relations 425 -- 14.4 Fuzzy Logic and Fuzzy Inference Systems 429 -- 14.5 Multifactorial Evaluation 433 -- 14.6 Extracting Fuzzy Models from Data 436 -- 14.7 Data Mining and Fuzzy Sets 441 -- 14.8 Review Questions and Problems 443 -- 14.9 References for Further Study 445 -- 15 VISUALIZATION METHODS 447 -- 15.1 Perception and Visualization 448 -- 15.2 Scientifi c Visualization and -- Information Visualization 449 -- 15.3 Parallel Coordinates 455 -- 15.4 Radial Visualization 458 -- 15.5 Visualization Using Self-Organizing Maps (SOMs) 460 -- 15.6 Visualization Systems for Data Mining 462 -- 15.7 Review Questions and Problems 467 -- 15.8 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.
Record Nr. UNINA-9910138049703321
Kantardzic Mehmed  
Hoboken, New Jersey : , : John Wiley, , c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining : concepts, models, methods, and algorithms / / Mehmed Kantardzic
Data mining : concepts, models, methods, and algorithms / / Mehmed Kantardzic
Autore Kantardzic Mehmed
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley, , c2011
Descrizione fisica 1 online resource (554 p.)
Disciplina 005.741
006.3/12
Soggetto topico Data mining
ISBN 1-283-23974-4
9786613239747
1-118-02913-5
1-118-02912-7
1-118-02914-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface to the Second Edition xiii -- Preface to the First Edition xv -- 1 DATA-MINING CONCEPTS 1 -- 1.1 Introduction 1 -- 1.2 Data-Mining Roots 4 -- 1.3 Data-Mining Process 6 -- 1.4 Large Data Sets 9 -- 1.5 Data Warehouses for Data Mining 14 -- 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 -- 1.7 Organization of This Book 21 -- 1.8 Review Questions and Problems 23 -- 1.9 References for Further Study 24 -- 2 PREPARING THE DATA 26 -- 2.1 Representation of Raw Data 26 -- 2.2 Characteristics of Raw Data 31 -- 2.3 Transformation of Raw Data 33 -- 2.4 Missing Data 36 -- 2.5 Time-Dependent Data 37 -- 2.6 Outlier Analysis 41 -- 2.7 Review Questions and Problems 48 -- 2.8 References for Further Study 51 -- 3 DATA REDUCTION 53 -- 3.1 Dimensions of Large Data Sets 54 -- 3.2 Feature Reduction 56 -- 3.3 Relief Algorithm 66 -- 3.4 Entropy Measure for Ranking Features 68 -- 3.5 PCA 70 -- 3.6 Value Reduction 73 -- 3.7 Feature Discretization: ChiMerge Technique 77 -- 3.8 Case Reduction 80 -- 3.9 Review Questions and Problems 83 -- 3.10 References for Further Study 85 -- 4 LEARNING FROM DATA 87 -- 4.1 Learning Machine 89 -- 4.2 SLT 93 -- 4.3 Types of Learning Methods 99 -- 4.4 Common Learning Tasks 101 -- 4.5 SVMs 105 -- 4.6 kNN: Nearest Neighbor Classifi er 118 -- 4.7 Model Selection versus Generalization 122 -- 4.8 Model Estimation 126 -- 4.9 90% Accuracy: Now What? 132 -- 4.10 Review Questions and Problems 136 -- 4.11 References for Further Study 138 -- 5 STATISTICAL METHODS 140 -- 5.1 Statistical Inference 141 -- 5.2 Assessing Differences in Data Sets 143 -- 5.3 Bayesian Inference 146 -- 5.4 Predictive Regression 149 -- 5.5 ANOVA 155 -- 5.6 Logistic Regression 157 -- 5.7 Log-Linear Models 158 -- 5.8 LDA 162 -- 5.9 Review Questions and Problems 164 -- 5.10 References for Further Study 167 -- 6 DECISION TREES AND DECISION RULES 169 -- 6.1 Decision Trees 171 -- 6.2 C4.5 Algorithm: Generating a Decision Tree 173 -- 6.3 Unknown Attribute Values 180 -- 6.4 Pruning Decision Trees 184.
6.5 C4.5 Algorithm: Generating Decision Rules 185 -- 6.6 CART Algorithm & Gini Index 189 -- 6.7 Limitations of Decision Trees and Decision Rules 192 -- 6.8 Review Questions and Problems 194 -- 6.9 References for Further Study 198 -- 7 ARTIFICIAL NEURAL NETWORKS 199 -- 7.1 Model of an Artifi cial Neuron 201 -- 7.2 Architectures of ANNs 205 -- 7.3 Learning Process 207 -- 7.4 Learning Tasks Using ANNs 210 -- 7.5 Multilayer Perceptrons (MLPs) 213 -- 7.6 Competitive Networks and Competitive Learning 221 -- 7.7 SOMs 225 -- 7.8 Review Questions and Problems 231 -- 7.9 References for Further Study 233 -- 8 ENSEMBLE LEARNING 235 -- 8.1 Ensemble-Learning Methodologies 236 -- 8.2 Combination Schemes for Multiple Learners 240 -- 8.3 Bagging and Boosting 241 -- 8.4 AdaBoost 243 -- 8.5 Review Questions and Problems 245 -- 8.6 References for Further Study 247 -- 9 CLUSTER ANALYSIS 249 -- 9.1 Clustering Concepts 250 -- 9.2 Similarity Measures 253 -- 9.3 Agglomerative Hierarchical Clustering 259 -- 9.4 Partitional Clustering 263 -- 9.5 Incremental Clustering 266 -- 9.6 DBSCAN Algorithm 270 -- 9.7 BIRCH Algorithm 272 -- 9.8 Clustering Validation 275 -- 9.9 Review Questions and Problems 275 -- 9.10 References for Further Study 279 -- 10 ASSOCIATION RULES 280 -- 10.1 Market-Basket Analysis 281 -- 10.2 Algorithm Apriori 283 -- 10.3 From Frequent Itemsets to Association Rules 285 -- 10.4 Improving the Effi ciency of the Apriori Algorithm 286 -- 10.5 FP Growth Method 288 -- 10.6 Associative-Classifi cation Method 290 -- 10.7 Multidimensional Association-Rules Mining 293 -- 10.8 Review Questions and Problems 295 -- 10.9 References for Further Study 298 -- 11 WEB MINING AND TEXT MINING 300 -- 11.1 Web Mining 300 -- 11.2 Web Content, Structure, and Usage Mining 302 -- 11.3 HITS and LOGSOM Algorithms 305 -- 11.4 Mining Path-Traversal Patterns 310 -- 11.5 PageRank Algorithm 313 -- 11.6 Text Mining 316 -- 11.7 Latent Semantic Analysis (LSA) 320 -- 11.8 Review Questions and Problems 324 -- 11.9 References for Further Study 326.
12 ADVANCES IN DATA MINING 328 -- 12.1 Graph Mining 329 -- 12.2 Temporal Data Mining 343 -- 12.3 Spatial Data Mining (SDM) 357 -- 12.4 Distributed Data Mining (DDM) 360 -- 12.5 Correlation Does Not Imply Causality 369 -- 12.6 Privacy, Security, and Legal Aspects of Data Mining 376 -- 12.7 Review Questions and Problems 381 -- 12.8 References for Further Study 382 -- 13 GENETIC ALGORITHMS 385 -- 13.1 Fundamentals of GAs 386 -- 13.2 Optimization Using GAs 388 -- 13.3 A Simple Illustration of a GA 394 -- 13.4 Schemata 399 -- 13.5 TSP 402 -- 13.6 Machine Learning Using GAs 404 -- 13.7 GAs for Clustering 409 -- 13.8 Review Questions and Problems 411 -- 13.9 References for Further Study 413 -- 14 FUZZY SETS AND FUZZY LOGIC 414 -- 14.1 Fuzzy Sets 415 -- 14.2 Fuzzy-Set Operations 420 -- 14.3 Extension Principle and Fuzzy Relations 425 -- 14.4 Fuzzy Logic and Fuzzy Inference Systems 429 -- 14.5 Multifactorial Evaluation 433 -- 14.6 Extracting Fuzzy Models from Data 436 -- 14.7 Data Mining and Fuzzy Sets 441 -- 14.8 Review Questions and Problems 443 -- 14.9 References for Further Study 445 -- 15 VISUALIZATION METHODS 447 -- 15.1 Perception and Visualization 448 -- 15.2 Scientifi c Visualization and -- Information Visualization 449 -- 15.3 Parallel Coordinates 455 -- 15.4 Radial Visualization 458 -- 15.5 Visualization Using Self-Organizing Maps (SOMs) 460 -- 15.6 Visualization Systems for Data Mining 462 -- 15.7 Review Questions and Problems 467 -- 15.8 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.
Record Nr. UNINA-9910830267803321
Kantardzic Mehmed  
Hoboken, New Jersey : , : John Wiley, , c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui