A STEP TOWARDS THE MAJORITY-BASED CLUSTERING VALIDATION DECISION FUSION METHOD

Abstract

A variety of clustering validation indices (CVIs) aimed at validating the results of clustering analysis and determining which clustering algorithm performs best. Different validation indices may be appropriate for different clustering algorithms or partition dissimilarity measures; however, the best suitable index to use in practice remains unknown. A single CVI is generally unable to handle the wide variability and scalability of the data and cope successfully with all the contexts. Therefore, one of the popular approaches is to use a combination of multiple CVIs and fuse their votes into the final decision. The aim of this work is to analyze the majority-based decision fusion method. Thus, the experimental work consisted of designing and implementing the NbClust majority-based decision fusion method and then evaluating the CVIs performance with different clustering algorithms and dissimilarity measures in order to discover the best validation configuration. Moreover, the author proposed to enhance the standard majority-based decision fusion method with straightforward rules for the maximum efficiency of the validation procedure. The result showed that the designed enhanced method with an invasive validation configuration could cope with almost all data sets (99%) with different experimental factors (density, dimensionality, number of clusters, etc.).