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Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber
Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber
Autore Han Jiawei
Edizione [2nd ed.]
Pubbl/distr/stampa Amsterdam ; ; London, : Elsevier, c2006
Descrizione fisica 1 online resource (772 p.)
Disciplina 005.741
Altri autori (Persone) KamberMicheline
Collana The Morgan Kaufmann series in data management systems
Soggetto topico Data mining
Computer science
Soggetto genere / forma Electronic books.
ISBN 1-282-66586-3
9786612665868
0-08-047558-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front cover; Title page; Copyright page; Dedication; Table of contents; Foreword; Preface; Organization of the Book; To the Instructor; To the Student; To the Professional; Book Websites with Resources; Acknowledgments for the First Edition of the Book; Acknowledgments for the Second Edition of the Book; 1 Introduction; 1.1 What Motivated Data Mining? Why Is It Important?; 1.2 So, What Is Data Mining?; 1.3 Data Mining-On What Kind of Data?; 1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?; 1.5 Are All of the Patterns Interesting?; 1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives1.8 Integration of a Data Mining System with a Database or Data Warehouse System; 1.9 Major Issues in Data Mining; 1.10 Summary; Exercises; Bibliographic Notes; 2 Data Preprocessing; 2.1 Why Preprocess the Data?; 2.2 Descriptive Data Summarization; 2.3 Data Cleaning; 2.4 Data Integration and Transformation; 2.5 Data Reduction; 2.6 Data Discretization and Concept Hierarchy Generation; 2.7 Summary; Exercises; Bibliographic Notes; 3 Data Warehouse and OLAP Technology: An Overview; 3.1 What Is a Data Warehouse?; 3.2 A Multidimensional Data Model
3.3 Data Warehouse Architecture3.4 Data Warehouse Implementation; 3.5 From Data Warehousing to Data Mining; 3.6 Summary; Exercises; Bibliographic Notes; 4 Data Cube Computation and Data Generalization; 4.1 Efficient Methods for Data Cube Computation; 4.2 Further Development of Data Cube and OLAP Technology; 4.3 Attribute-Oriented Induction-An Alternative Method for Data Generalization and Concept Description; 4.4 Summary; Exercises; Bibliographic Notes; 5 Mining Frequent Patterns, Associations, and Correlations; 5.1 Basic Concepts and a Road Map
5.2 Efficient and Scalable Frequent Itemset Mining Methods5.3 Mining Various Kinds of Association Rules; 5.4 From Association Mining to Correlation Analysis; 5.5 Constraint-Based Association Mining; 5.6 Summary; Exercises; Bibliographic Notes; 6 Classification and Prediction; 6.1 What Is Classification? What Is Prediction?; 6.2 Issues Regarding Classification and Prediction; 6.3 Classification by Decision Tree Induction; 6.4 Bayesian Classification; 6.5 Rule-Based Classification; 6.6 Classification by Backpropagation; 6.7 Support Vector Machines
6.8 Associative Classification: Classification by Association Rule Analysis6.9 Lazy Learners (or Learning from Your Neighbors); 6.10 Other Classification Methods; 6.11 Prediction; 6.12 Accuracy and Error Measures; 6.13 Evaluating the Accuracy of a Classifier or Predictor; 6.14 Ensemble Methods-Increasing the Accuracy; 6.15 Model Selection; 6.16 Summary; Exercises; Bibliographic Notes; 7 Cluster Analysis; 7.1 What Is Cluster Analysis?; 7.2 Types of Data in Cluster Analysis; 7.3 A Categorization of Major Clustering Methods; 7.4 Partitioning Methods; 7.5 Hierarchical Methods
7.6 Density-Based Methods
Record Nr. UNINA-9910451443203321
Han Jiawei  
Amsterdam ; ; London, : Elsevier, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber
Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber
Autore Han Jiawei
Edizione [2nd ed.]
Pubbl/distr/stampa Amsterdam ; ; London, : Elsevier, c2006
Descrizione fisica 1 online resource (772 p.)
Disciplina 005.741
Altri autori (Persone) KamberMicheline
Collana The Morgan Kaufmann series in data management systems
Soggetto topico Data mining
Computer science
ISBN 9780080475585
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front cover; Title page; Copyright page; Dedication; Table of contents; Foreword; Preface; Organization of the Book; To the Instructor; To the Student; To the Professional; Book Websites with Resources; Acknowledgments for the First Edition of the Book; Acknowledgments for the Second Edition of the Book; 1 Introduction; 1.1 What Motivated Data Mining? Why Is It Important?; 1.2 So, What Is Data Mining?; 1.3 Data Mining-On What Kind of Data?; 1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?; 1.5 Are All of the Patterns Interesting?; 1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives1.8 Integration of a Data Mining System with a Database or Data Warehouse System; 1.9 Major Issues in Data Mining; 1.10 Summary; Exercises; Bibliographic Notes; 2 Data Preprocessing; 2.1 Why Preprocess the Data?; 2.2 Descriptive Data Summarization; 2.3 Data Cleaning; 2.4 Data Integration and Transformation; 2.5 Data Reduction; 2.6 Data Discretization and Concept Hierarchy Generation; 2.7 Summary; Exercises; Bibliographic Notes; 3 Data Warehouse and OLAP Technology: An Overview; 3.1 What Is a Data Warehouse?; 3.2 A Multidimensional Data Model
3.3 Data Warehouse Architecture3.4 Data Warehouse Implementation; 3.5 From Data Warehousing to Data Mining; 3.6 Summary; Exercises; Bibliographic Notes; 4 Data Cube Computation and Data Generalization; 4.1 Efficient Methods for Data Cube Computation; 4.2 Further Development of Data Cube and OLAP Technology; 4.3 Attribute-Oriented Induction-An Alternative Method for Data Generalization and Concept Description; 4.4 Summary; Exercises; Bibliographic Notes; 5 Mining Frequent Patterns, Associations, and Correlations; 5.1 Basic Concepts and a Road Map
5.2 Efficient and Scalable Frequent Itemset Mining Methods5.3 Mining Various Kinds of Association Rules; 5.4 From Association Mining to Correlation Analysis; 5.5 Constraint-Based Association Mining; 5.6 Summary; Exercises; Bibliographic Notes; 6 Classification and Prediction; 6.1 What Is Classification? What Is Prediction?; 6.2 Issues Regarding Classification and Prediction; 6.3 Classification by Decision Tree Induction; 6.4 Bayesian Classification; 6.5 Rule-Based Classification; 6.6 Classification by Backpropagation; 6.7 Support Vector Machines
6.8 Associative Classification: Classification by Association Rule Analysis6.9 Lazy Learners (or Learning from Your Neighbors); 6.10 Other Classification Methods; 6.11 Prediction; 6.12 Accuracy and Error Measures; 6.13 Evaluating the Accuracy of a Classifier or Predictor; 6.14 Ensemble Methods-Increasing the Accuracy; 6.15 Model Selection; 6.16 Summary; Exercises; Bibliographic Notes; 7 Cluster Analysis; 7.1 What Is Cluster Analysis?; 7.2 Types of Data in Cluster Analysis; 7.3 A Categorization of Major Clustering Methods; 7.4 Partitioning Methods; 7.5 Hierarchical Methods
7.6 Density-Based Methods
Record Nr. UNINA-9910784150603321
Han Jiawei  
Amsterdam ; ; London, : Elsevier, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining : concepts and techniques / / Jiawei Han, Micheline Kamber
Data mining : concepts and techniques / / Jiawei Han, Micheline Kamber
Autore Han Jiawei
Edizione [2nd ed.]
Pubbl/distr/stampa Amsterdam ; ; London, : Elsevier, c2006
Descrizione fisica 1 online resource (772 p.)
Disciplina 005.741
Altri autori (Persone) KamberMicheline
Collana The Morgan Kaufmann series in data management systems
Soggetto topico Data mining
Computer science
ISBN 9780080475585
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front cover; Title page; Copyright page; Dedication; Table of contents; Foreword; Preface; Organization of the Book; To the Instructor; To the Student; To the Professional; Book Websites with Resources; Acknowledgments for the First Edition of the Book; Acknowledgments for the Second Edition of the Book; 1 Introduction; 1.1 What Motivated Data Mining? Why Is It Important?; 1.2 So, What Is Data Mining?; 1.3 Data Mining-On What Kind of Data?; 1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?; 1.5 Are All of the Patterns Interesting?; 1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives1.8 Integration of a Data Mining System with a Database or Data Warehouse System; 1.9 Major Issues in Data Mining; 1.10 Summary; Exercises; Bibliographic Notes; 2 Data Preprocessing; 2.1 Why Preprocess the Data?; 2.2 Descriptive Data Summarization; 2.3 Data Cleaning; 2.4 Data Integration and Transformation; 2.5 Data Reduction; 2.6 Data Discretization and Concept Hierarchy Generation; 2.7 Summary; Exercises; Bibliographic Notes; 3 Data Warehouse and OLAP Technology: An Overview; 3.1 What Is a Data Warehouse?; 3.2 A Multidimensional Data Model
3.3 Data Warehouse Architecture3.4 Data Warehouse Implementation; 3.5 From Data Warehousing to Data Mining; 3.6 Summary; Exercises; Bibliographic Notes; 4 Data Cube Computation and Data Generalization; 4.1 Efficient Methods for Data Cube Computation; 4.2 Further Development of Data Cube and OLAP Technology; 4.3 Attribute-Oriented Induction-An Alternative Method for Data Generalization and Concept Description; 4.4 Summary; Exercises; Bibliographic Notes; 5 Mining Frequent Patterns, Associations, and Correlations; 5.1 Basic Concepts and a Road Map
5.2 Efficient and Scalable Frequent Itemset Mining Methods5.3 Mining Various Kinds of Association Rules; 5.4 From Association Mining to Correlation Analysis; 5.5 Constraint-Based Association Mining; 5.6 Summary; Exercises; Bibliographic Notes; 6 Classification and Prediction; 6.1 What Is Classification? What Is Prediction?; 6.2 Issues Regarding Classification and Prediction; 6.3 Classification by Decision Tree Induction; 6.4 Bayesian Classification; 6.5 Rule-Based Classification; 6.6 Classification by Backpropagation; 6.7 Support Vector Machines
6.8 Associative Classification: Classification by Association Rule Analysis6.9 Lazy Learners (or Learning from Your Neighbors); 6.10 Other Classification Methods; 6.11 Prediction; 6.12 Accuracy and Error Measures; 6.13 Evaluating the Accuracy of a Classifier or Predictor; 6.14 Ensemble Methods-Increasing the Accuracy; 6.15 Model Selection; 6.16 Summary; Exercises; Bibliographic Notes; 7 Cluster Analysis; 7.1 What Is Cluster Analysis?; 7.2 Types of Data in Cluster Analysis; 7.3 A Categorization of Major Clustering Methods; 7.4 Partitioning Methods; 7.5 Hierarchical Methods
7.6 Density-Based Methods
Record Nr. UNINA-9910811362503321
Han Jiawei  
Amsterdam ; ; London, : Elsevier, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining : concepts and techniques / Jiawei Han, Micheline Kamber
Data mining : concepts and techniques / Jiawei Han, Micheline Kamber
Autore Han, Jiawei
Edizione [2. ed]
Pubbl/distr/stampa Amsterdam [etc.], : Elsevier
Descrizione fisica XXVIII, 770 p. : ill. ; 24 cm.
Disciplina 005.74
005.741
Altri autori (Persone) Kamber, Micheline
Collana The Morgan Kaufmann series in data management systems
Soggetto topico Archivi di dati
ISBN 1558609016
9781558609013
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISANNIO-USM1614368
Han, Jiawei  
Amsterdam [etc.], : Elsevier
Materiale a stampa
Lo trovi qui: Univ. del Sannio
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-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
Data warehouse design : modern principles and methodologies / Matteo Golfarelli, Stefano Rizzi ; [foreword by Mark Stephen LaRow] ; translated by Claudio Pagliarani
Data warehouse design : modern principles and methodologies / Matteo Golfarelli, Stefano Rizzi ; [foreword by Mark Stephen LaRow] ; translated by Claudio Pagliarani
Autore GOLFARELLI, Matteo
Pubbl/distr/stampa New York [etc.], : McGraw-Hill, 2009
Descrizione fisica XXI, 458 p. : ill. ; 23 cm
Disciplina 005.741
Altri autori (Persone) RIZZI, Stefano
Soggetto topico Archivi di dati
ISBN 9780071610391
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione und
Record Nr. UNISA-996434749603316
GOLFARELLI, Matteo  
New York [etc.], : McGraw-Hill, 2009
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Data warehousing fundamentals [[electronic resource] ] : a comprehensive guide for IT professionals / / Paulraj Ponniah
Data warehousing fundamentals [[electronic resource] ] : a comprehensive guide for IT professionals / / Paulraj Ponniah
Autore Ponniah Paulraj
Edizione [1st edition]
Pubbl/distr/stampa New York, : Wiley, c2001
Descrizione fisica 1 online resource (544 p.)
Disciplina 005.741
658.4/038/0285574
Soggetto topico Data warehousing
Information technology
ISBN 1-280-36744-X
9786610367443
0-470-35648-0
1-118-09717-3
0-471-22162-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto DATA WAREHOUSING FUNDAMENTALS; CONTENTS; Foreword; Preface; Part 1 OVERVIEW AND CONCEPTS; Part 2 PLANNING AND REQUIREMENTS; Part 3 ARCHITECTURE AND INFRASTRUCTURE; Part 4 DATA DESIGN AND DATA PREPARATION; Part 5 INFORMATION ACCESS AND DELIVERY; Part 6 IMPLEMENTATION AND MAINTENANCE; Appendix A. Project Life Cycle Steps and Checklists; Appendix B. Critical Factors for Success; Appendix C. Guidelines for Evaluating Vendor Solutions; References; Glossary; Index
Record Nr. UNINA-9910142499803321
Ponniah Paulraj  
New York, : Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Autore Dasu Tamraparni
Pubbl/distr/stampa New York, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (226 p.)
Disciplina 005.741
006.3
006.312
Altri autori (Persone) JohnsonTheodore
Collana Wiley series in probability and statistics
Soggetto topico Data mining
Electronic data processing - Data preparation
Electronic data processing - Quality control
Soggetto genere / forma Electronic books.
ISBN 1-280-36625-7
9786610366255
0-470-30781-1
0-471-45864-3
0-471-44835-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Exploratory Data Mining and Data Cleaning; Contents; Preface; 1. Exploratory Data Mining and Data Cleaning: An Overview; 1.1 Introduction; 1.2 Cautionary Tales; 1.3 Taming the Data; 1.4 Challenges; 1.5 Methods; 1.6 EDM; 1.6.1 EDM Summaries-Parametric; 1.6.2 EDM Summaries-Nonparametric; 1.7 End-to-End Data Quality (DQ); 1.7.1 DQ in Data Preparation; 1.7.2 EDM and Data Glitches; 1.7.3 Tools for DQ; 1.7.4 End-to-End DQ: The Data Quality Continuum; 1.7.5 Measuring Data Quality; 1.8 Conclusion; 2. Exploratory Data Mining; 2.1 Introduction; 2.2 Uncertainty; 2.2.1 Annotated Bibliography
2.3 EDM: Exploratory Data Mining2.4 EDM Summaries; 2.4.1 Typical Values; 2.4.2 Attribute Variation; 2.4.3 Example; 2.4.4 Attribute Relationships; 2.4.5 Annotated Bibliography; 2.5 What Makes a Summary Useful?; 2.5.1 Statistical Properties; 2.5.2 Computational Criteria; 2.5.3 Annotated Bibliography; 2.6 Data-Driven Approach-Nonparametric Analysis; 2.6.1 The Joy of Counting; 2.6.2 Empirical Cumulative Distribution Function (ECDF); 2.6.3 Univariate Histograms; 2.6.4 Annotated Bibliography; 2.7 EDM in Higher Dimensions; 2.8 Rectilinear Histograms; 2.9 Depth and Multivariate Binning
2.9.1 Data Depth2.9.2 Aside: Depth-Related Topics; 2.9.3 Annotated Bibliography; 2.10 Conclusion; 3. Partitions and Piecewise Models; 3.1 Divide and Conquer; 3.1.1 Why Do We Need Partitions?; 3.1.2 Dividing Data; 3.1.3 Applications of Partition-Based EDM Summaries; 3.2 Axis-Aligned Partitions and Data Cubes; 3.2.1 Annotated Bibliography; 3.3 Nonlinear Partitions; 3.3.1 Annotated Bibliography; 3.4 DataSpheres (DS); 3.4.1 Layers; 3.4.2 Data Pyramids; 3.4.3 EDM Summaries; 3.4.4 Annotated Bibliography; 3.5 Set Comparison Using EDM Summaries; 3.5.1 Motivation; 3.5.2 Comparison Strategy
3.5.3 Statistical Tests for Change3.5.4 Application-Two Case Studies; 3.5.5 Annotated Bibliography; 3.6 Discovering Complex Structure in Data with EDM Summaries; 3.6.1 Exploratory Model Fitting in Interactive Response Time; 3.6.2 Annotated Bibliography; 3.7 Piecewise Linear Regression; 3.7.1 An Application; 3.7.2 Regression Coefficients; 3.7.3 Improvement in Fit; 3.7.4 Annotated Bibliography; 3.8 One-Pass Classification; 3.8.1 Quantile-Based Prediction with Piecewise Models; 3.8.2 Simulation Study; 3.8.3 Annotated Bibliography; 3.9 Conclusion; 4. Data Quality; 4.1 Introduction
4.2 The Meaning of Data Quality4.2.1 An Example; 4.2.2 Data Glitches; 4.2.3 Conventional Definition of DQ; 4.2.4 Times Have Changed; 4.2.5 Annotated Bibliography; 4.3 Updating DQ Metrics: Data Quality Continuum; 4.3.1 Data Gathering; 4.3.2 Data Delivery; 4.3.3 Data Monitoring; 4.3.4 Data Storage; 4.3.5 Data Integration; 4.3.6 Data Retrieval; 4.3.7 Data Mining/Analysis; 4.3.8 Annotated Bibliography; 4.4 The Meaning of Data Quality Revisited; 4.4.1 Data Interpretation; 4.4.2 Data Suitability; 4.4.3 Dataset Type; 4.4.4 Attribute Type; 4.4.5 Application Type
4.4.6 Data Quality-A Many Splendored Thing
Record Nr. UNINA-9910146077903321
Dasu Tamraparni  
New York, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Autore Dasu Tamraparni
Pubbl/distr/stampa New York, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (226 p.)
Disciplina 005.741
006.3
006.312
Altri autori (Persone) JohnsonTheodore
Collana Wiley series in probability and statistics
Soggetto topico Data mining
Electronic data processing - Data preparation
Electronic data processing - Quality control
ISBN 1-280-36625-7
9786610366255
0-470-30781-1
0-471-45864-3
0-471-44835-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Exploratory Data Mining and Data Cleaning; Contents; Preface; 1. Exploratory Data Mining and Data Cleaning: An Overview; 1.1 Introduction; 1.2 Cautionary Tales; 1.3 Taming the Data; 1.4 Challenges; 1.5 Methods; 1.6 EDM; 1.6.1 EDM Summaries-Parametric; 1.6.2 EDM Summaries-Nonparametric; 1.7 End-to-End Data Quality (DQ); 1.7.1 DQ in Data Preparation; 1.7.2 EDM and Data Glitches; 1.7.3 Tools for DQ; 1.7.4 End-to-End DQ: The Data Quality Continuum; 1.7.5 Measuring Data Quality; 1.8 Conclusion; 2. Exploratory Data Mining; 2.1 Introduction; 2.2 Uncertainty; 2.2.1 Annotated Bibliography
2.3 EDM: Exploratory Data Mining2.4 EDM Summaries; 2.4.1 Typical Values; 2.4.2 Attribute Variation; 2.4.3 Example; 2.4.4 Attribute Relationships; 2.4.5 Annotated Bibliography; 2.5 What Makes a Summary Useful?; 2.5.1 Statistical Properties; 2.5.2 Computational Criteria; 2.5.3 Annotated Bibliography; 2.6 Data-Driven Approach-Nonparametric Analysis; 2.6.1 The Joy of Counting; 2.6.2 Empirical Cumulative Distribution Function (ECDF); 2.6.3 Univariate Histograms; 2.6.4 Annotated Bibliography; 2.7 EDM in Higher Dimensions; 2.8 Rectilinear Histograms; 2.9 Depth and Multivariate Binning
2.9.1 Data Depth2.9.2 Aside: Depth-Related Topics; 2.9.3 Annotated Bibliography; 2.10 Conclusion; 3. Partitions and Piecewise Models; 3.1 Divide and Conquer; 3.1.1 Why Do We Need Partitions?; 3.1.2 Dividing Data; 3.1.3 Applications of Partition-Based EDM Summaries; 3.2 Axis-Aligned Partitions and Data Cubes; 3.2.1 Annotated Bibliography; 3.3 Nonlinear Partitions; 3.3.1 Annotated Bibliography; 3.4 DataSpheres (DS); 3.4.1 Layers; 3.4.2 Data Pyramids; 3.4.3 EDM Summaries; 3.4.4 Annotated Bibliography; 3.5 Set Comparison Using EDM Summaries; 3.5.1 Motivation; 3.5.2 Comparison Strategy
3.5.3 Statistical Tests for Change3.5.4 Application-Two Case Studies; 3.5.5 Annotated Bibliography; 3.6 Discovering Complex Structure in Data with EDM Summaries; 3.6.1 Exploratory Model Fitting in Interactive Response Time; 3.6.2 Annotated Bibliography; 3.7 Piecewise Linear Regression; 3.7.1 An Application; 3.7.2 Regression Coefficients; 3.7.3 Improvement in Fit; 3.7.4 Annotated Bibliography; 3.8 One-Pass Classification; 3.8.1 Quantile-Based Prediction with Piecewise Models; 3.8.2 Simulation Study; 3.8.3 Annotated Bibliography; 3.9 Conclusion; 4. Data Quality; 4.1 Introduction
4.2 The Meaning of Data Quality4.2.1 An Example; 4.2.2 Data Glitches; 4.2.3 Conventional Definition of DQ; 4.2.4 Times Have Changed; 4.2.5 Annotated Bibliography; 4.3 Updating DQ Metrics: Data Quality Continuum; 4.3.1 Data Gathering; 4.3.2 Data Delivery; 4.3.3 Data Monitoring; 4.3.4 Data Storage; 4.3.5 Data Integration; 4.3.6 Data Retrieval; 4.3.7 Data Mining/Analysis; 4.3.8 Annotated Bibliography; 4.4 The Meaning of Data Quality Revisited; 4.4.1 Data Interpretation; 4.4.2 Data Suitability; 4.4.3 Dataset Type; 4.4.4 Attribute Type; 4.4.5 Application Type
4.4.6 Data Quality-A Many Splendored Thing
Record Nr. UNISA-996211655103316
Dasu Tamraparni  
New York, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui