Analysis of K-Mean Algorithm

Authors(2) :-Vinaya Durga M, Ganapathi Sharma K

Clustering is one among the foremost common preliminary knowledge associates to analysis technique to get an intuition regarding the structure of the info. It is often outlined because the task of characteristic subgroups within the knowledge such knowledge points within the same subgroup (cluster) area unit are similar whereas knowledge points totally different in numerous clusters area different. There are several algorithms which deals with unsupervised learning. K means algorithm is one of such algorithm. Kmeans algorithm is an iterative algorithm that tries to partition the dataset into K pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the inter-cluster data points as similar as possible while also keeping the clusters as far as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid , that is (x2-x1)2+ (y2-y1)2

Authors and Affiliations

Vinaya Durga M
Assistant Professor, St Aloysius College, Mangalore, Karnataka, India
Ganapathi Sharma K
Associate Professor, Shrinivas University, Mangalore, Karnataka, India

Kmean, Cluster, Datamining, Hierarchical

Publication Details

Published in : Volume 6 | Issue 1 | January-February 2020
Date of Publication : 2020-02-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 133-136
Manuscript Number : CSEIT206126
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Vinaya Durga M, Ganapathi Sharma K, "Analysis of K-Mean Algorithm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 1, pp.133-136, January-February-2020. |          | BibTeX | RIS | CSV

Article Preview