Analysis of K-Mean Clustering For Various Data Sets In MATLAB

Authors(2) :-Varsha Bansal, Mahesh Kumar

Data mining is a process that uses different technologies, using "patterns" or "knowledge" in the data. These are some data mining techniques related to classification, clustering, and association principles. Among them there are some "similar" things between them, called "balanced" Belongs to other clusters. This means using cluster analysis to find things projects in one group will be different from projects and other group objects. Is a cluster Neutral learning skills. Neutral learning means learning first without knowledge Sample rating K-minerals are a specific way of cluster observations. "K" indicates the number of specific clusters. There are different distance steps to determine Observe the cluster. The purpose of the algorithm is to minimize Measurements between cluster centers and their given observations Track observations in any cluster and eliminate shortest distance measurements Get, finish. This proposed work having global data sets work on MATLAB which found very effectively when it come to underlying structure of modified K-mean. This will have the capability of finding the cluster area efficiently.

Authors and Affiliations

Varsha Bansal
Ganga Institute of Technology and Management, MDU Rohtak, Maharashtra, India
Mahesh Kumar
Assistant. Professor, Department of Computer Science and Engineering, GITAM, MDU Rohtak, Maharashtra, India

K-Mean, Global Variables , MATLAB.

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 139-143
Manuscript Number : CSEIT183550
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Varsha Bansal, Mahesh Kumar, "Analysis of K-Mean Clustering For Various Data Sets In MATLAB", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.139-143, May-June-2018.
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