ENHANCED K-MEANS CLUSTRING ALGORITHM TO REDUCE TIME COMPLEXITY FOR NUMERIC VALUES

Authors

  • Bhoomi Bangoria PG Scholar [C.E.], Noble Engineering College, bhoomi.bangoria@gmail.com
  • Prof. Nirali Mankad Assistant Professor [C.E.], Noble Engineering College
  • Prof. Vimal Pambhar Assistant Professor [C.E.], Dr. Subhash Technical Campus

Keywords:

Centroid, Clustering, Enhanced k-means algorithm, K-means algorithm, Partitioning

Abstract

Data mining is the process of using technology to identify patterns and prospects from large
amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups.
K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters
in which each observation belongs to the cluster with the nearest mean. In this paper, try to improve the
performance, to find initial centroid systematically and distribute each data point to the suitable clusters.

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Published

2014-05-25

How to Cite

Bhoomi Bangoria, Prof. Nirali Mankad, & Prof. Vimal Pambhar. (2014). ENHANCED K-MEANS CLUSTRING ALGORITHM TO REDUCE TIME COMPLEXITY FOR NUMERIC VALUES. International Journal of Advance Engineering and Research Development (IJAERD), 1(5), 152–160. Retrieved from https://ijaerd.org/index.php/IJAERD/article/view/52

Issue

Vol. 1 No. 5 (2014): Volume 1 Issue 5 (May-2014)

Section

Articles