Pattern Recognition Algorithms for Data Mining
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A01=Pabitra Mitra
A01=Sankar K. Pal
advanced data analysis
Author_Pabitra Mitra
Author_Sankar K. Pal
Category=UYQM
Competitive Layer
computational intelligence
computing
Condensation Algorithm
Data Sets
dimensionality reduction
Discernibility Matrix
Em Algorithm
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
feature
Feature Selection
Feature Selection Algorithm
Feature Similarity Measures
Frequent Itemsets
function
Fuzzification Parameters
fuzzy
granular
granular computing applications
Graph Theoretic Clustering
GrC
hybrid learning models
Information Granules
KDD Process
Linguistic Fuzzy Sets
machine learning theory
membership
Membership Functions
Mst
rough
Rough Set
Rough Set Theory
Rule Extraction
Selforganizing Map
set
Soft Computing
Soft Computing Tools
space
support vector machines
theory
Unsupervised Feature Selection
Vowel Data
Product details
- ISBN 9781584884576
- Weight: 680g
- Dimensions: 156 x 234mm
- Publication Date: 27 May 2004
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
Pal, Sankar K.; Mitra, Pabitra
