Data Mining and Knowledge Discovery Handbook

This handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified whole. The book first surveys, then provides comprehensive yet concise algorithmic descriptions of classic methods plus recently-developed extensions and novel methods. The 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 is designed for research scientists and graduate-level computer science and engineering students. It is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Covers over 25 new topics, as well as most updated information on topics presented in first edition

Includes over 30 new world wide contributors, who are experts in this field

New case studies introduced based on real world examples

Includes supplementary material: sn.pub/extras

Autorentext

Prof. Oded Maimon is the Oracle chaired Professor at Tel-Aviv University, Previously at MIT. Oded is a leader expert in the field of data mining and knowledge discovery. He published many articles on new algorithms and seven significant award winning books in the field since 2000. He has also developed and implemented successful applications in the Industry. He heads an international research group sponsored by European Union awards.

Dr. Lior Rokach is a senior lecturer at the Department of Information System Engineering at Ben-Gurion University. He is a recognized expert in intelligent information systems and has held several leading positions in this field. His main areas of interest are Data Mining, Pattern Recognition, and Recommender Systems. Dr. Rokach is the author of over 70 refereed papers in leading journals, conference proceedings and book chapters. In addition he has authored six books and edited three others books.

Inhalt
Introduction to knowledge discovery in databases.- Part I Preprocessing methods.- Data cleansing.- Handling missing attribute values.- Geometric methods for feature extraction and dimensional reduction.- Dimension Reduction and feature selection.- Discretization methods.- outlier detection.- Part II Supervised methods.- Introduction to supervised methods.- Decision trees.- Bayesian networks.- Data mining within a regression framework.- Support vector machines.- Rule induction.- Part III Unsupervised methods.- Visualization and data mining for high dimensional datasets.- Clustering methods.- Association rules.- Frequent set mining.- Constraint-based data mining.- Link analysis.- Part IV Soft computing methods.- Evolutionary algorithms for data mining.- Reinforcement-learning: an overview from a data mining perspective.- Neural networks.- On the use of fuzzy logic in data mining.- Granular computing and rough sets.- Part V Supporting methods.- Statistical methods for data mining.- Logics for data mining.- Wavelet methods in data mining.- Fractal mining.- Interestingness measures.- Quality assessment approaches in data mining.- Data mining model comparison.- Data mining query languages.- Part VI Advanced methods.- Meta-learning.- Bias vs variance decomposition for regression and classification.- Mining with rare cases.- Mining data streams.- Mining high-dimensional data.- Text mining and information extraction.- Spatial data mining.- Data mining for imbalanced datasets: an overview.- Relational data mining.- Web mining.- A review of web document clustering approaches.- Causal discovery.- Ensemble methods for classifiers.- Decomposition methodology for knowledge discovery and data mining.- Information fusion.- Parallel and grid-based data mining.- Collaborative data mining.- Organizational data mining.- Mining time series data.- Part VII Applications.- Data mining in medicine.- Learning information patterns in biological databases.- Data mining for selection of manufacturing processes.- Data mining of design products and processes.- Data mining in telecommunications.- Data mining for financial applications.- Data mining for intrusion detection.- Data mining for software testing.- Data mining for CRM.- Data mining for target marketing.- Part VIII Software.- Oracle data mining.- Building data mining solutions with OLE DB for DM and XML for analysis.- LERSA data mining system.- GainSmarts data mining system for marketing.- WizSoft's WizWhy.- DataEngine.- Index.