Experimental work on Data Clustering using Enhanced Random K-Mode Algorithm

Abstract

Clustering the uncertainty data is not an easy task but an essential task in data mining. The traditional algorithms like K-Means clustering, UK Means clustering, density based clustering etc, to cluster uncertain data are limited to using geometric distance based similarity measures and cannot capture the difference between uncertain data with their distributions. Such methods cannot handle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very different variances in customer ratings. Because of its complexity, the clustering takes high execution time resulting in high computational cost. In this proposed method is Enhanced Random K-Mode algorithm which is also called as ERK-Mode to cluster the uncertainty data. The K-mode concept classifies the dataset and separates as certain and uncertain data from the whole dataset. Again enhanced random K-Mode is used to cluster the uncertainty data. The Weather data values are taken in to the account for experiments. The experiment shows that the proposed algorithm is very efficient with fast execution time and low complexity.