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Cluster Analysis in Data Mining using K-Means Method

by Narander Kumar, Vishal Verma, Vipin Saxena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 12
Year of Publication: 2013
Authors: Narander Kumar, Vishal Verma, Vipin Saxena
10.5120/13298-0748

Narander Kumar, Vishal Verma, Vipin Saxena . Cluster Analysis in Data Mining using K-Means Method. International Journal of Computer Applications. 76, 12 ( August 2013), 11-14. DOI=10.5120/13298-0748

@article{ 10.5120/13298-0748,
author = { Narander Kumar, Vishal Verma, Vipin Saxena },
title = { Cluster Analysis in Data Mining using K-Means Method },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 12 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number12/13298-0748/ },
doi = { 10.5120/13298-0748 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:45:42.416360+05:30
%A Narander Kumar
%A Vishal Verma
%A Vipin Saxena
%T Cluster Analysis in Data Mining using K-Means Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 12
%P 11-14
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To find the unknown and hidden pattern from large amount of data of insurance organizations. There are strong customer base required with the help of large database. Cluster Analysis is an excellent statistical tool for a large and multivariate database. The clusters analysis with K-Means method may be used to develop the model which is useful to find the relationship in a database. In this paper, consider the data of LIC customer, the seeds are the first three customers then compute the distance from cluster using the attributes of customers with the help of Clustering with K-Means method. Comparing the mean distance of cluster with the seeds. Finally, we find the nigh distances from the cluster as the cluster (C1) have three customers named S1, S2, S10 which are satisfy with all the benefits, terms and conditions of cluster (C1). If requirements of any customer same as the S1, S2, S10 then we allocated the cluster (C1). It will increase the revenue as well as profit of the organization with customer satisfaction.

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Index Terms

Computer Science
Information Sciences

Keywords

K-means methods Seeds Clustering analysis Cluster distance LIPS

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