Efficient Cluster Validation with K-Family Clusters on Quality Assessment
This paper presented an efficient cluster validation scheme for quality assessment of k-family cluster specifically to fuzzy k-means algorithm. The cluster quality index is measured in terms of number of clusters, number of objects in each cluster, cluster object cohesiveness, precision and recall values. The approach adapted in fuzzy k-means is based on heuristic method which iterates the cluster to form efficient valid clusters. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. The cluster validation criterion is introduced to find the optimal input metrics for fuzzy k-means algorithm. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters.