An Agglomerative Hierarchical Clustering Framework for Improving the Ensemble Clustering Process

Abstract

Agglomerative Hierarchical Clustering (AHC) is a general type of Hierarchical Clustering (HC) that forms clusters from the “bottom-up.” This paper focuses on the development of AHC methods based on ensemble-based approaches. Accordingly, we develop an AHC framework based on clusters clustering along with an innovative similarity criterion that performs clustering through ensemble approaches. The proposed algorithm consists of three main steps. In the first step, a group of single AHC methods are combined to detect relationships between samples and as well as the formation of initial clusters. The similarity of the samples is calculated using an innovative similarity criterion based on the clusters created. In the second step, all the initial clusters created by different methods are re-clustered to form hyper-clusters. After clusters clustering, each sample is assigned to a hyper-cluster with maximum similarity to create the final clusters in the third step. The comprehensive experimental study has been performed to evaluate the performance of the proposed algorithm based on several benchmark datasets from the UCI machine learning repository. The results clearly show that the proposed ensemble AHC-based framework performs better than the state-of-the-art methods.