Manuscript Number : CSEIT1831242
Case Study on Static k-Means Clustering Algorithm
Authors(1) :-Dr. Chatti Subba Lakshmi Data clustering is frequent research problem in many data mining applications. In this view, many clustering methods proposed in literature. One type of clustering is partitioning method which is centroid based technique. In this paper we are presenting the case study on conventional or static k-means partition clustering algorithm. Here we used static means the basic input parameter given to k-means is number of cluster (k), which constant for complete execution of data set. We need to decide the k values before algorithm starts and It does not changes, when there is a change in data set. We considered the some cases like distance measures, what is right number of clusters and relations between the algorithm parameters. We executed k-means algorithm on small data set and large data set and we presented the detailed steps for each case by showing the results
Dr. Chatti Subba Lakshmi Data Clustering, Partitioning Clustering, K-Means Clustering Algorithm, Static Publication Details Published in : Volume 3 | Issue 1 | January-February 2018 Article Preview
Department of CSE, Guru Nanak Institutions, Hyderabad, India
Date of Publication : 2018-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1160-1167
Manuscript Number : CSEIT1831242
Publisher : Technoscience Academy