Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World Wide Web, Social Media and Intelligent Systems. This data can be very important and useful if it is harnessed carefully and correctly. Useful information can be extracted from this massive data using the Data Mining process. The information extracted can be used to make vital decisions in various industries. Clustering is a very popular Data Mining method which divides the data points into different groups such that all similar data points form a part of the same group. Clustering methods are of various types. Many parameters and indexes exist for the evalua-tion and comparison of these methods. In this paper, we have compared partition-ing based methods K-Means, Fuzzy C-Means (FCM), Partitioning Around Medoids (PAM) and Clustering Large Application (CLARA) on secure perturbed data. Comparison and identification has been done for the method which performs better for analyzing the data perturbed using Extended NMF on the basis of the values of various indexes like Dunn Index, Silhouette Index, Xie-Beni Index and Davies-Bouldin Index.

Automated summary

This paper compares different clustering methods on secure perturbed data to identify the method that performs better for analyzing data perturbed using Extended NMF, based on various indexes such as Dunn Index, Silhouette Index, Xie-Beni Index, and Davies-Bouldin Index.