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Effect of Distance Functions on Simple K-means Clustering Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 49 - Number 6 |
| Year of Publication: 2012 |
| Authors: Richa Loohach, Kanwal Garg |
10.5120/7629-0698
|
Richa Loohach, Kanwal Garg . Effect of Distance Functions on Simple K-means Clustering Algorithm. International Journal of Computer Applications. 49, 6 ( July 2012), 7-9. DOI=10.5120/7629-0698
Abstract
Clustering analysis is the most significant step in data mining. This paper discusses the k-means clustering algorithm and various distance functions used in k-means clustering algorithm such as Euclidean distance function and Manhattan distance function. Experimental results are shown to observe the effect of Manhattan distance function and Euclidean distance function on k-means clustering algorithm. These results also show that distance functions furthermore affect the size of clusters formed by the k-means clustering algorithm.
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