Ioannis Sara, Phil Trinder, and Ali Zalzala School of Mathematics and...  Home/Search
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Abstract: In this paper, we present a novel evolutionary algorithm, called NOCEA, which is suitable for Data Mining (DM) clustering applications. (Update)
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BibTeX entry: (Update)
@misc{ phil-mining,
author = "Ioannis Sara Phil",
title = "Mining Comprehensive Clustering Rules with an Evolutionary Algorithm",
url = "citeseer.ist.psu.edu/630141.html" }
Citations (may not include all citations):2138 Genetic Algorithms in Search (context) - Goldberg - 1989
805 Algorithms for Clustering Data (context) - Jain, Dubes - 1988
475 Automatic subspace clustering of high dimensional data for d.. - Agrawal, Gehrke et al. - 1998
225 Data Mining: Concepts and Techniques (context) - Han, Kamber - 2000
206 Genetic Algorithms + Data Structures = Evolution Programs (context) - Michalewicz - 1996
149 Multivariate Density Estimation (context) - Scott - 1992
50 BIRCH: an ecient data clustering method for very large datab.. (context) - Zhang, Ramakrishnan et al.
30 CURE: An ecient clustering algorithm for large databases (context) - Guha, Rastogi et al. - 1998
24 Chameleon: Hierarchical clustering using dynamic modeling (context) - Karypis, Han et al. - 1999
22 Clustering with a genetically optimized approach - Hall, Ozyurt et al. - 1999
8 An ecient approach to clustering in large multimedia databas.. (context) - Hinneburg, Keim - 1998
2 A genetic rule-based data clustering toolkit - Sara, Zalzala et al. - 2002
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