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Data Mining and Knowledge Discovery Handbook

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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.

Author(s): Oded Maimon, Lior Rokach (auth.), Oded Maimon, Lior Rokach (eds.)
Series: Texts and Monographs in Physics
Edition: 2
Publisher: Springer US
Year: 2010

Language: English
Pages: 1285
Tags: Database Management; Information Storage and Retrieval; Artificial Intelligence (incl. Robotics); Information Systems Applications (incl.Internet); Information Systems and Communication Service

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