Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2000

Abstract

This paper investigates the scalability of predictive Adaptive Resonance Theory (ART) networks for knowledge discovery in very large databases. Although predictive ART performs fast and incremental learning, the number of recognition categories or rules that it creates during learning may become substantially large and cause the learning speed to slow down. To tackle this problem, we introduce an on-line algorithm for evaluating and pruning categories during learning. Benchmark experiments on a large scale data set show that on-line pruning has been effective in reducing the number of the recognition categories and the time for convergence. Interestingly, the pruned networks also produce better predictive performance.

Keywords

Adaptive Resonance Theory, Category Node, Algorithm, Benchmark Experiment, Pattern Pair

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

PAKDD 2000: Knowledge Discovery and Data Mining: Current Issues and New Applications, 11-14 May, Singapore: Proceedings pp 173-176 | Cite as

Volume

1805

First Page

173

Last Page

176

ISBN

9783540673828

Identifier

10.1007/3-540-45571-X_21

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/3-540-45571-X_21

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