Data Mining and Knowledge Discovery 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 repository.
This book 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 is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Language
Place of publication
Target group
Professional and scholarly
Research
Illustrations
400
400 s/w Abbildungen
400 black & white illustrations
Dimensions
Height: 23.5 cm
Width: 15.5 cm
ISBN-13
978-0-387-24435-8 (9780387244358)
DOI
Schweitzer Classification
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.- LERS-A data mining system.- GainSmarts data mining system for marketing.- WizSoft's WizWhy.- DataEngine.- Index.
