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

作　　者：Neetika Bhandari Payal Pahwa 

机构地区：[1]Guru Gobind Singh Indraprastha University,Delhi,India [2]Bhagwan Parshuram Institute of Technology,Delhi,India

出　　处：《Intelligent Automation & Soft Computing》2023年第2期2043-2055,共13页智能自动化与软计算（英文）

摘　　要：Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

关 键 词：Clustering CLARA Davies-Bouldin index Dunn index FCM intelligent systems K-means non-negative matrix factorization(NMF) PAM privacy preserving data mining Silhouette index Xie-Beni index 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]