Pattern Recognition Algorithms for Data Mining Scalability, Knowledge Discovery and Soft Granular Computing Chapman & Hall/CRC Computer Science & Data Analysis Series
Auteurs : Pal Sankar K., Mitra Pabitra
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.
Date de parution : 09-2019
15.6x23.4 cm
Date de parution : 05-2004
Ouvrage de 244 p.
15.6x23.4 cm
Thèmes de Pattern Recognition Algorithms for Data Mining :
Mots-clés :
Data Sets; Rough Set Theory; Rough Set; Feature Similarity Measures; Information Granules; KDD Process; Selforganizing Map; Linguistic Fuzzy Sets; Frequent Itemsets; GrC; Membership Functions; Discernibility Matrix; Feature Selection; Rule Extraction; Soft Computing Tools; Vowel Data; Graph Theoretic Clustering; Em Algorithm; Fuzzification Parameters; Condensation Algorithm; Feature Selection Algorithm; Unsupervised Feature Selection; Mst; Soft Computing; Competitive Layer