Metric Based Performance Analysis of Clustering Algorithms for High Dimensional Data

Abstract

Cluster analysis is a main task of exploratory data mining and plays important role in many applications. There are numerous of clustering techniques in data mining works efficiently for low dimensional data and fails to handle high dimensional data. In this paper we evaluated the performance efficiency of K-means and Agglomerative hierarchical clustering methods based on Euclidean and Manhattan distance functions for high dimensional data by varying cluster values. Efficiency concerns the computational time required to build up datasets. Based on experimental work we examined that in both case of distance functions Agglomerative clustering method is efficient in time than K-means clustering algorithm on dataset, which we use for empirical study.