Efficient Quality Assessment Technique with Integrated Cluster Validation and Decision Trees
Clustering becomes a key technique in analyzing quality assessment in most of the recent research works. The partitioned clustering techniques used in previous work utilize attributes of objects to form cluster. The cluster numbers were initialized, which reduces cluster quality in terms of cluster object aggregation and appropriation. The paper presented an efficient quality assessment technique comprising of two parts i.e., fuzzy K-means cluster validation scheme and decision tree model. The fuzzy K-means cluster validation scheme improves recall and precision measure of automatically labeling cluster objects.