Pattern Recognition Algorithms for Data Mining - Scalability, Knowledge Discovery and Soft Granular Computing

Informationen zum Autor Pal, Sankar K.; Mitra, Pabitra Klappentext Pattern Recognition Algorithms for Data Mining addresses 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. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. The authors proceed to analyze the tasks of multi-scale data condensation and dimensionality reduction. Then they explore the problem of learning with support vector machine (SVM), and conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm. Zusammenfassung Addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. It concludes by highlighting the significance of granular computing for different mining tasks in a soft paradigm. Inhaltsverzeichnis Introduction. Multiscale data condensation. Unsupervised feature selection. Active learning using support vector machine. Rough-fuzzy case generation. Rough-fuzzy clustering. Rough self-organizing map. Classification, rule generation and evaluation using modular rough-fuzzy MLP. Appendices.

Product details

• Authors: Pabitra Mitra, Mitra Pabitra, Pal K. Pal, Sankar K. Pal, Sankar K. (Indian Statistical Institute Pal, Sankar K. Mitra Pal, Pal Sankar K.
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 27.05.2004
• EAN: 9781584884576
• ISBN: 978-1-58488-457-6
• No. of pages: 274
• Series: Chapman & Hall/CRC Computer Science & Data Analysis; Chapman & Hall/CRC Computer Sc; Chapman & Hall/CRC Computer Science & Data Analysis
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Operating systems, user interfaces; machine learning, COMPUTERS / Data Science / Machine Learning