Cluster validity index based on Jeffrey divergence
This work addresses the need for more accurate cluster evaluation metrics in data analysis, though it appears incremental as it modifies an existing index component.
The authors tackled the problem of evaluating clustering results by proposing a new cluster validity index that uses Jeffrey divergence to measure separation between clusters, and experimental results showed it outperformed widely used indexes.
Cluster validity indexes are very important tools designed for two purposes: comparing the performance of clustering algorithms and determining the number of clusters that best fits the data. These indexes are in general constructed by combining a measure of compactness and a measure of separation. A classical measure of compactness is the variance. As for separation, the distance between cluster centers is used. However, such a distance does not always reflect the quality of the partition between clusters and sometimes gives misleading results. In this paper, we propose a new cluster validity index for which Jeffrey divergence is used to measure separation between clusters. Experimental results are conducted using different types of data and comparison with widely used cluster validity indexes demonstrates the outperformance of the proposed index.