International Journal of Computer
Trends and Technology

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Volume 4 | Issue 4 | Year 2013 | Article Id. IJCTT-V4I4P166 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I4P166

A Comparative study on clustering of data using Improved K-means Algorithms

Abhilash C B, Sharana basavanagowda

Citation :

Abhilash C B, Sharana basavanagowda, "A Comparative study on clustering of data using Improved K-means Algorithms," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 4, pp. 771-778, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I4P166

Abstract

There exist many algorithms for clustering, and most widely used is K-means algorithm as it is easy to understand and simulate on different datasets. In our paper work we have used K-means algorithm for clustering of yeast dataset and iris datasets, in which clustering resulted in less accuracy with more number of iterations. We are simulating an improved version of K-means algorithm for clustering of these datasets, the Improved K-means algorithm use the technique of minimum spanning tree. An undirected graph is generated for all the input data points and then shortest distance is calculated which intern results in better accuracy and also with less number of iterations. Both algorithms have been simulated using java programming language; the results obtained from both algorithms are been compared and analysed. Algorithms have been run for several times under different clustering groups and the analysis results showed that the Improved K-means algorithm has provided a better performance as compared to K-means algorithm; also Improved K-means algorithm showed that, as the number of cluster values increases the accuracy of the algorithm also increases. Also we have inferred from the results that at a particular value of K (cluster groups) the accuracy of Improved K-means algorithm is optimal.

Keywords

K-Means, MST, Improved K-Means, Yeast dataset, iris dataset.

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