Cluster-Based Cumulative Ensembles

Abstract. In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching criterion based on maximum similarity is used. The ensemble method is investigated with bootstrap re-sampling, where the k-means algorithm is used to generate high granularity clusterings. For combining, group average hierarchical metaclustering is applied and the Jaccard measure is used for cluster similarity computation. Patterns are assigned to combined meta-clusters based on estimated cluster assignment probabilities. The cluster-based cumulative ensembles are more compact than co-association-based ensembles. Experimental results on artificial and real data show reduction of the error rate across varying ensemble parameters and cluster structures.

• Type: Conference
• Year: 2005
• Where: MCS
• Authors: Hanan Ayad, Mohamed S. Kamel
• Keywords: Cluster Ensembles, Cluster-based Cumulative Ensembles, Cluster-based Cumulative Representation, MCS 2005, Pattern Recognition