Pattern Recognition Algorithms for Data Mining / Edition 1

Pattern Recognition Algorithms for Data Mining / Edition 1

ISBN-10:
1584884576
ISBN-13:
9781584884576
Pub. Date:
05/27/2004
Publisher:
Taylor & Francis
ISBN-10:
1584884576
ISBN-13:
9781584884576
Pub. Date:
05/27/2004
Publisher:
Taylor & Francis
Pattern Recognition Algorithms for Data Mining / Edition 1

Pattern Recognition Algorithms for Data Mining / Edition 1

$170.0 Current price is , Original price is $170.0. You
$170.00 
  • SHIP THIS ITEM
    Qualifies for Free Shipping
  • PICK UP IN STORE
    Check Availability at Nearby Stores
  • SHIP THIS ITEM

    Temporarily Out of Stock Online

    Please check back later for updated availability.


Overview

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.

Product Details

ISBN-13: 9781584884576
Publisher: Taylor & Francis
Publication date: 05/27/2004
Series: Chapman & Hall/CRC Computer Science & Data Analysis , #3
Edition description: New Edition
Pages: 274
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Pal, Sankar K.; Mitra, Pabitra

Table of Contents

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.
From the B&N Reads Blog

Customer Reviews