Details

to Knowledge Discovery in Databases.- to Knowledge Discovery in Databases.- 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.- Supervised Methods.- to Supervised Methods.- Decision Trees.- Bayesian Networks.- Data Mining within a Regression Framework.- Support Vector Machines.- Rule Induction.- Unsupervised Methods.- Visualization and Data Mining for High Dimensional Datasets.- Clustering Methods.- Association Rules.- Frequent Set Mining.- Constraint-Based Data Mining.- Link Analysis.- 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.- Supporting Methods.- Statistical Methods for Data Mining.- Logics for Data Mining.- Wavelet Methods in Data Mining.- Fractal Mining.- Interesting Measures.- Quality Assessment Approaches in Data Mining.- Data Mining Model Comparison.- Data Mining Query Languages.- 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.- Modelling medical diagnostic rules based on rough sets.- Data Mining in Medicine.- The statistical analysis of contingency table designs.- Learning Information Patterns in Biological Databases.- Computer Integrated Manufacturing: A Data Mining Approach.- Data Mining for Selection of Manufacturing Processes.- Learning expert systems in numerical analysis of structures.- Data Mining of Design Products and Processes.- ANSWER: Network monitoring using object-oriented rule.- Data Mining in Telecommunications.- Knowledge Discovery for Gene Regulatory Regions Analysis.- Data Mining for Financial Applications.- Data Mining for Intrusion Detection.- Data Mining for Intrusion Detection.- Fuzzy Cluster Analysis: Methods for Classification.- Data Mining for Software Testing.- Data Mining for CRM.- Data Mining for CRM.- Learning Internal Representation by Error Propagation.- Data Mining for Target Marketing.- Software.- Weka.- 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.

<P><STRONG>Data Mining and Knowledge Discovery Handbook</STRONG> 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. </P>
<P>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.</P>
<P><STRONG>Data Mining and Knowledge Discovery Handbook</STRONG> 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.</P>

Most complete, extensive and modern handbook available today in the field of data mining, the core of the knowledge discovery process Algorithmic descriptions are detailed so the reader can understand exactly how they work, and thus implement, modify and intelligently use them Includes detailed tutorials, and each topic is supplemented with references for further study Includes supplementary material: sn.pub/extras

<P>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.</P>