Introduces pattern recognition (PR) concepts and tasks including scalability to large data sets and knowledge discovery in databases (KDD) Describes methodologi... » 

Introduces pattern recognition (PR) concepts and tasks including scalability to large data sets and knowledge discovery in databases (KDD) Describes methodologies for multiscale data condensation and unsupervised dimentionality reduction for large data Presents active learning strategies for handling a large quadratic problem in an SVM frameworkDeploys a rough-fuzzy framework for case generation and for clustering large data setsDescribes design procedure of a rough self-organizing map (RSOM)Applies fuzzy sets, rough sets, neural nets, and genetic algorithms for problems of classification, rule generation, and evaluation in a supervised modeProvides experimental results on real life data along with their sources Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm. «