Pattern Recognition Algorithms for Data Mining
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, me
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Multiscale Data Condensation
Unsupervised Feature Selection
Active Learning Using Support Vector Machine
Roughfuzzy Case Generation
Rough SelfOrganizing Map
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Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge ...
Sankar K. Pal,Pabitra Mitra (PhD.)
No preview available - 2004
approach association rules attributes cancer CEMMiSTRI classification accuracy compared Comparison component condensation algorithms condensation ratio condensed set corresponding data condensation data mining database decision denotes density estimation dependency rules described dimensional domain encoding entropy error evaluation feature selection algorithms feature space Figure Forest cover type FSFS fuzzy sets Gaussians genetic algorithms granular computing granules hyperplane IEEE input iteration k-means k-means algorithm k-NN knowledge discovery large data sets linear Machine Learning Mean SD Mean measure membership functions method methodology multiscale nearest neighbor neural networks neuro-fuzzy nodes objects obtained optimal output partitioning pattern recognition performance problem provides quantization reduced regions represent representation rough set theory RSOM rule extraction samples scale SD Mean SD Section selection algorithms self-organizing map soft computing statistical query StatQSVM subnetworks subset support vector support vector machine Table tasks techniques tion unsupervised variables Vowel data weights