Performance Evaluation of Clustering Methods for Low and High Dimensional Data

Abstract

Various Data Clustering techniques are used for grouping similar data and play an important role in the field of Data Mining. Most of the clustering techniques are work good for clustering low dimensional data.We focus on comparitive study of clustering methods: K-means,Hierarchical and Density based clustering, for performance with low and high dimensional data. Experimental results evaluate the performance of these methods on different datasets by analyzing number of features ,number of clusters and time required for clustering data set.

Keywords: Clustering, K-means, Hierarchical, Density-based, Weka