ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and SoftwarePublished 1 September 2004
Cheng-Fa Tsai, Chun‐Wei Tsai, Han‐Chang Wu, Tzer Yang
Citations73
SJR quartileQ1
SJR score0.97
SNIP2.01
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Abstract
In this paper, we present an efficient clustering approach for large databases. Our simulation results indicate that the proposed novel clustering method (called ant colony optimization with different favor algorithm) performs better than the fast self-organizing map (SOM) combines K-means approach (FSOM+K-means) and genetic K-means algorithm (GKA). In addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+K-means approach and GKA.
Keywords
Computer Science
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