An Efficient Cluster Formation Approach for Handling Large Scale Data

Anil ¹ , Rajendra Gupta²

Section:Research Paper, Product Type: Journal-Paper
Vol.5 , Issue.1 , pp.1-6, Dec-2018

Online published on Dec 31, 2018

Copyright © Anil, Rajendra Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The basic K-means algorithm is the most widely used clustering algorithm and the best-known of the partitioning-based clustering methods. Choosing the initial centroids randomly results in poor clustering. Instead of optimal clustering (or global optimum), sub-optimal clustering (or local minimum) is obtained. One of the problems associated with the basic K-means algorithm is that empty clusters can be obtained if no points are allocated to a cluster during the assignment step. So implementation in basic k-mean algorithm gives better solution for large scale data analysis. In this paper, the k-mean algorithm and its implementation have done and the obtained results are also discussed. After the analysis, it is found that the implementation in k-mean algorithm provide better outcome.

Key-Words / Index Term :
Basic k-mean, implemented k-mean, Large Scale Data, Fuzzy C4.5

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