Yoking of Algorithms for Effective Clustering

K  Mohana Prasad   and R  Sabitha

doi:10.17485/ijst/2015/v8i22/79106

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Yoking of Algorithms for Effective Clustering

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2015/v8i22/79106

Year: 2015, Volume: 8, Issue: 22, Pages: 1-4

Original Article

Yoking of Algorithms for Effective Clustering

K. Mohana Prasad^1* and R. Sabitha²

¹Department of CSE, Sathyabama University, Chennai - 600119, Tamil Nadu, India; [email protected]
²Department of IT, Jeppiaar Engineering College, Chennai - 600119, Tamil Nadu, India;
[email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Cluster plays a vital and very important in data mining. Cluster is a main and absolute part of real time applications. Grouping an object with its own class is known as Cluster. It has two different segments, Similar and Dissimilar objects. K Mean (KM) is one of the exclusive clustering algorithms. K Mean algorithm is introduced by cluster, which forms an easier and simpler way of classifying a given set of data. This paper is clearly based on Gravitational Search Algorithm (GSA) and KM algorithm. The main advantage of GSA and KM algorithm is to escape local optima and make convergence motions in rapid progression. A main five data sets in an UCI repository is used to bring the results and solutions in an excellent way using these algorithms. This paper aims to bring an exclusive and efficient result from both the algorithms compared to other algorithm and also gives perfect solution for the existing set of data.
Keywords: GSA, K Mean, UCI Repository