| Pattern Recognition Algorithms for Data Mining Contributor(s): Pal, Sankar K. (Author), Mitra, Pabitra (Author), Pal, Pal K. (Author) |
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ISBN: 1584884576 ISBN-13: 9781584884576 Publisher: Routledge OUR PRICE: $142.50 Product Type: Hardcover - Other Formats Published: May 2004 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Databases - Data Mining - Mathematics | Arithmetic - Computers | Programming - Algorithms |
| Dewey: 006.312 |
| LCCN: 2004043539 |
| Series: Chapman & Hall/CRC Computer Science & Data Analysis |
| Physical Information: 0.81" H x 6.46" W x 9.46" (1.16 lbs) 274 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |