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.
Table of contents:
Introduction to Knowledge Discovery in Databases.- Data Cleansing.- Handling Missing Values.- Feature Extraction and Dimensional Reduction.- Discretization Methods.- Outlier Detection.- . Decision Trees.- Bayesian Networks.- Regression.- Support Vector Machine0073.- Rule Induction.- Visualization.- Clustering.- Association Rules.- Link Analysis.- Evolutionary Algorithms.- Neural Networks.- Fuzzy Logic.- Granular Computing and Rough Sets.- Statistical Methods.- Wavelet Methods.- Fractal Mining.- Interestingness Measures.- Quality Assessment.- Query Languages.- Mining Data Streams.- Text Mining and Information Extraction.- Spatial DM.- Relational DM.- Web Mining.- Causal Discovery.- Ensemble Methods.- Decomposition Methods.- Information Fusion.- Parallel DM.- Time Series DM.- Medical Applications.- Biological Applications.- Manufacturing Applications.- Design Applications.- Telecommunications Applications.- Financial Applications.- Intrusion Detection.- Software Testing.- CRM Applications.- Marketing Applications.- Weka.- Oracle DM.- OLE DB DM.
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.
Table of contents:
Introduction to Knowledge Discovery in Databases.- Data Cleansing.- Handling Missing Values.- Feature Extraction and Dimensional Reduction.- Discretization Methods.- Outlier Detection.- . Decision Trees.- Bayesian Networks.- Regression.- Support Vector Machine0073.- Rule Induction.- Visualization.- Clustering.- Association Rules.- Link Analysis.- Evolutionary Algorithms.- Neural Networks.- Fuzzy Logic.- Granular Computing and Rough Sets.- Statistical Methods.- Wavelet Methods.- Fractal Mining.- Interestingness Measures.- Quality Assessment.- Query Languages.- Mining Data Streams.- Text Mining and Information Extraction.- Spatial DM.- Relational DM.- Web Mining.- Causal Discovery.- Ensemble Methods.- Decomposition Methods.- Information Fusion.- Parallel DM.- Time Series DM.- Medical Applications.- Biological Applications.- Manufacturing Applications.- Design Applications.- Telecommunications Applications.- Financial Applications.- Intrusion Detection.- Software Testing.- CRM Applications.- Marketing Applications.- Weka.- Oracle DM.- OLE DB DM.