A data partitioning approach for hierarchical clustering
Seok-Ho Yoon, Suk-Soon Song, Sangchul Lee, Kyosung Jeong, Sang‐Wook Kim, Sooyong Kang, Yong Suk Choi, Jaehyuk Cha, Minsoo Ryu, Byung-Soo Jeong · 2013
In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.