Effect of using principal coordinates and principal components on retrieval of clusters

Abstract

Principal coordinate analysis is a more powerful technique than principal component analysis to ensure identification on groups of objects if some conditions are satisfied. The results of using principal coordinates prior to cluster analysis were investigated. Three different methods of standardization were examined and compared with no standardization using both principal coordinates and principal components. The retrieval abilities of the known agglomerative clustering algorithms were improved by using principal coordinates. The results of applying principal coordinates based on the correlation coefficient instead of Euclidean distance prior to clustering algorithms were less sensitive to changes in noise.