A hybridized approach to data clustering

Data clustering helps one discern the structure of and simplify the complexity of massive quantities of data. It is a common technique for statistical data analysis and is used in many fields, including machine learning, data mining, pattern recognition, image analysis, and bioinformatics, in which the distribution of information can be of any size and shape. The well-known K-means algorithm, which has been successfully applied to many practical clustering problems, suffers from several drawbacks due to its choice of initializations. A hybrid technique based on combining the K-means algorithm, Nelder-Mead simplex search, and particle swarm optimization, called K-NM-PSO, is proposed in this research. The K-NM-PSO searches for cluster centers of an arbitrary data set as does the K-means algorithm, but it can effectively and efficiently find the global optima. The new K-NM-PSO algorithm is tested on nine data sets, and its performance is compared with those of PSO, NM-PSO, K-PSO and K-means clustering. Results show that K-NM-PSO is both robust and suitable for handling data clustering.