Wasp swarm optimization of the c-means clustering model

✍ Scribed by Thomas A. Runkler

• Publisher: John Wiley and Sons
• Year: 2008
• Tongue: English
• Weight: 443 KB
• Volume: 23
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.20266

✦ Synopsis

This paper deals with clustering by optimizing the c-means clustering model. For some data sets this clustering model possesses many local optima, so conventional alternating optimization (AO) will produce bad results. For obtaining good clustering results, the minimization procedure has to be kept from being trapped in these local optima, for example, by stochastic optimization approaches. Recently, we showed that ant colony optimization (ACO) can be effectively applied to the c-means clustering model. In this paper, we introduce a wasp swarm optimization (WSO) algorithm to optimize the c-means clustering model. In experiments with four benchmark data sets, the new WSO clustering algorithm is compared with AO and ACO. For data sets leading to c-means models without local optima, both WSO and AO perform better and faster than ACO. For data sets leading to multiple local optima, WSO clearly outperforms both AO and ACO.