An Investigation of Effects on Hierarchical Clustering of Distance Measurements

Clustering is an important tool for a variety of applications in data mining, statistical data analysis, data compression, and vector quantization. The goal of clustering is to group data into clusters such that the similarities among data members within the same cluster are maximal while similarities among data members from different clusters are minimal. There are a number of distance measures that have been used as similarity measures. Distance measures play an important role in cluster analysis. The choice of distance measure is extremely important and should not be taken lightly. In this study, the effects of the distance measurements on hierarchical clustering will be investigated. For this purposes 8 different data sets commonly used in the literature and 2 data sets generated from multivariate normal distribution will be used.