Volume 08 Issue 02, March 2020

Survey on Optimizing Initial Cluster Center Based on Data Analysis Using K-Means Clustering Algorithm

Chetali Makode, Kedar Nath Singh

Page no:01-04

Abstract

As a partition primarily based bunch algorithmic rule, K-Means is wide utilized in several areas for the options of its efficiency and simply understood. However, it's documented that the K-Means algorithmic rule could get suboptimal solutions, depending on the selection of the initial cluster centers. During this paper, they propose a projection-based K-Means data format algorithmic rule. The planned algorithmic rule initially uses a typical mathematician kernel density estimation technique to search out the extremely density information areas in one dimension. Then the projection step is to iteratively use density estimation from the lower variance dimensions to the upper variance ones till all the scale is computed. Experiments on actual datasets show that our technique will get similar results compared with different typical strategies with fewer computation tasks this paper reviews numerous strategies and techniques utilized in literature and its benefits and limitations, to research the more would like of improvement of the k-means algorithmic rule. Planned algorithmic rule (PAVSM) increased information analysis.