A method of two stage clustering using agglomerative hierarchical algorithms with one-pass k-means++ or k-median++
Yusuke Tamura, Sadaaki Miyamoto · 2014
The aim of this paper is to propose a two-stage method of clustering in which the first stage uses one-pass k-median++ and the second stage uses an agglomerative hierarchical clustering. To handle medians in the second stage, we proposed two calculation methods. One method uses L1distance as similarity. Another uses error of L1distance like the Ward method. In this paper, we compared proposed method and a two-stage method of our study which uses k-means++ in the first stage to examine the effectiveness of L1distance in two-stage methods. Numerical experiments have been done using two criteria: objective function values and the Rand index.