Towards semi-supervised ensemble clustering using a new membership similarity measure

Abstract

Hierarchical clustering is a common type of clustering in which the dataset is hierarchically divided and represented by a dendrogram. Agglomerative Hierarchical Clustering (AHC) is a common type of hierarchical clustering in which clusters are created bottom-up. In addition, semi-supervised clustering is a new method in the field of machine learning, where supervised and unsupervised learning are combined. Clustering performance is effectively improved by semi-supervised learning, as it uses a small amount of labelled data to aid unsupervised learning. Meanwhile, ensemble clustering by combining the results of several individual clustering methods can achieve better performance compared to each of the individual methods. Considering AHC with semi-supervised learning for ensemble clustering configuration has received less attention in the past literature. In order to achieve better clustering results, we propose a semi-supervised ensemble clustering framework developed based on AHC-based methods. Here, we develop a flexible weighting mechanism along with a new membership similarity measure that can establish compatibility between semi-supervised clustering methods. We evaluated the proposed method with several equivalent methods based on a wide variety of UCI datasets. Experimental results show the effectiveness of the proposed method from different aspects such as NMI, ARI and accuracy.

Keywords

• AHC
• ensemble clustering
• membership similarity measure
• semi-supervised clustering