Cluster Validation Using Splitting and Merging Technique
Asit Kumar Das, Jaya Sil · 2007
Analysis of voluminous data generated over the years by business houses, genome projects or elsewhere reveals important findings that advances research activity in respective fields. Grouping meaningful relevant data easily identifies patterns performing similar functions. Clustering is one of the most important techniques used for this purpose. However, obtaining correct number of stable clusters is still an unsolved problem. The proposed method, not sensitive to initialization, generates a set of clusters using the input datasets. The clusters are validated using splitting and merging technique in order to obtain optimal set of clusters. It has been tested on electronic shopping data sets and results are discussed.