2019, Vol. 4, Issue 3, Part A
Effect of changing the number of parameters in a dataset on the result of k-means clustering algorithm
Author(s): Prabhu Pant, Hrishikesh Vallabh Joshi, Pankaj Joshi and Sanjay Joshi
Abstract: One of the main analytical techniques in data mining nowadays is clustering analysis. Clustering analysis is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. One of the most common clustering algorithms is the k-means algorithm. This paper is a study of effect of changing the number of parameters in a dataset on the result of k-means clustering algorithm.. Experimental results show that there is a considerable saving in runtime without affecting the results if the input data points are appropriately chosen.
Pages: 43-46 | Views: 865 | Downloads: 15Download Full Article: Click HereHow to cite this article:
Prabhu Pant, Hrishikesh Vallabh Joshi, Pankaj Joshi, Sanjay Joshi. Effect of changing the number of parameters in a dataset on the result of k-means clustering algorithm. Int J Stat Appl Math 2019;4(3):43-46.
