Data Mining and Knowledge Discovery Handbook

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. __Data Mining and Knowledge Discovery Handbook, Second Edition__ organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. __Data Mining and Knowledge Discovery Handbook, Second Edition__ is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management. Front Matter....Pages i-xx Introduction to Knowledge Discovery and Data Mining....Pages 1-15 Front Matter....Pages 17-17 Data Cleansing: A Prelude to Knowledge Discovery....Pages 19-32 Handling Missing Attribute Values....Pages 33-51 Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour....Pages 53-82 Dimension Reduction and Feature Selection....Pages 83-100 Discretization Methods....Pages 101-116 Outlier Detection....Pages 117-130 Front Matter....Pages 131-131 Supervised Learning....Pages 133-147 Classification Trees....Pages 149-174 Bayesian Networks....Pages 175-208 Data Mining within a Regression Framework....Pages 209-230 Support Vector Machines....Pages 231-247 Rule Induction....Pages 249-265 Front Matter....Pages 267-267 A survey of Clustering Algorithms....Pages 269-298 Association Rules....Pages 299-319 Frequent Set Mining....Pages 321-338 Constraint-based Data Mining....Pages 339-354 Link Analysis....Pages 355-368 Front Matter....Pages 369-369 A Review of Evolutionary Algorithms for Data Mining....Pages 371-400 A Review of Reinforcement Learning Methods....Pages 401-417 Front Matter....Pages 369-369 Neural Networks For Data Mining....Pages 419-444 Granular Computing and Rough Sets - An Incremental Development....Pages 445-468 Pattern Clustering Using a Swarm Intelligence Approach....Pages 469-504 Using Fuzzy Logic in Data Mining....Pages 505-520 Front Matter....Pages 521-521 Statistical Methods for Data Mining....Pages 523-540 Logics for Data Mining....Pages 541-551 Wavelet Methods in Data Mining....Pages 553-571 Fractal Mining - Self Similarity-based Clustering and its Applications....Pages 573-589 Visual Analysis of Sequences Using Fractal Geometry....Pages 591-601 Interestingness Measures - On Determining What Is Interesting....Pages 603-612 Quality Assessment Approaches in Data Mining....Pages 613-639 Data Mining Model Comparison....Pages 641-654 Data Mining Query Languages....Pages 655-664 Front Matter....Pages 665-665 Mining Multi-label Data....Pages 667-685 Privacy in Data Mining....Pages 687-716 Meta-Learning - Concepts and Techniques....Pages 717-731 Bias vs Variance Decomposition for Regression and Classification....Pages 733-746 Mining with Rare Cases....Pages 747-757 Data Stream Mining....Pages 759-787 Mining Concept-Drifting Data Streams....Pages 789-802 Front Matter....Pages 665-665 Mining High-Dimensional Data....Pages 803-808 Text Mining and Information Extraction....Pages 809-835 Spatial Data Mining....Pages 837-854 Spatio-temporal clustering....Pages 855-874 Data Mining for Imbalanced Datasets: An Overview....Pages 875-886 Relational Data Mining....Pages 887-911 Web Mining....Pages 913-929 A Review of Web Document Clustering Approaches....Pages 931-948 Causal Discovery....Pages 949-958 Ensemble Methods in Supervised Learning....Pages 959-979 Data Mining using Decomposition Methods....Pages 981-998 Information Fusion - Methods and Aggregation Operators....Pages 999-1008 Parallel and Grid-Based Data Mining – Algorithms, Models and Systems for High-Performance KDD....Pages 1009-1028 Collaborative Data Mining....Pages 1029-1039 Organizational Data Mining....Pages 1041-1048 Mining Time Series Data....Pages 1049-1077 Front Matter....Pages 1079-1079 Multimedia Data Mining....Pages 1081-1109 Data Mining in Medicine....Pages 1111-1136 Learning Information Patterns in Biological Databases - Stochastic Data Mining....Pages 1137-1152 Data Mining for Financial Applications....Pages 1153-1169 Front Matter....Pages 1079-1079 Data Mining for Intrusion Detection....Pages 1171-1180 Data Mining for CRM....Pages 1181-1188 Data Mining for Target Marketing....Pages 1189-1220 NHECD - Nano Health and Environmental Commented Database....Pages 1221-1241 Front Matter....Pages 1243-1243 Commercial Data Mining Software....Pages 1245-1268 Weka-A Machine Learning Workbench for Data Mining....Pages 1269-1277 Back Matter....Pages 1279-1285

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data.

Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.

Data Mining and Knowledge Discovery Handbook, Second Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management.

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered ́ this is the challenge created by today ́ s abundance of data. Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook, Second Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook, 2nd Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management. This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.