Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics

Abstract

The purpose of selective clustering ensemble is to select a subset of base clustering partitions with predictive performance and combine these partitions into more accurate and stable final results. Traditional approaches tend to utilize the well-known validity criteria such as NMI to evaluate the quality and diversity of base clustering partitions in the selection process. However, the characteristics of the original data and the data structure itself are commonly neglected. Furthermore, the generation process of base clustering partitions is more concerned with diversity and less consideration of quality. To tackle these problems, we propose a new selective clustering ensemble scheme. In the process of generating base clustering partitions, k-means and hierarchical clustering algorithm alternately combined with random projection method are employed to generate diverse base partitions. Meanwhile, in order to improve the quality of base clustering partitions, we propose a new selection strategy for the number of clusters $k$ in k-means algorithm. In the clustering selection process, both diversity and quality of the base clustering partitions are evaluated by multi-modal metrics from two levels: clustering labels and data structure. Based on five UCI benchmark datasets, experimental results demonstrate that the proposed method not only can generate but also select base clustering partitions with both diversity and quality. Experimental analyses show the validity and stability of the proposed scheme.