K-Means Divide and Conquer Clustering

Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. Most clustering techniques ignore the fact about the different size or levels - where in most cases, clustering is more concern with grouping similar objects or samples together ignoring the fact that even though they are similar, they might be of different levels. For really large data sets, data reduction should be performed prior to applying the data-mining techniques which is usually performing dimension reduction, and the main question is whether some of these prepared and preprocessed data can be discarded without sacrificing the quality of results. Existing clustering techniques would normally merge small clusters with big ones, removing its identity. In this study we propose a method which uses divide and conquer technique to improve the performance of the k-means clustering method.