Belief Functions and Cluster Ensembles

Abstract. In this paper, belief functions, defined on the lattice of partitions of a set of objects, are investigated as a suitable framework for combining multiple clusterings. We first show how to represent clustering results as masses of evidence allocated to partitions. Then a consensus belief function is obtained using a suitable combination rule. Tools for synthesizing the results are also proposed. The approach is illustrated using two data sets.

• Type: Conference
• Year: 2009
• Where: ECSQARU
• Authors: Marie-Hélène Masson, Thierry Denoeux
• Keywords: Artificial Intelligence, Belief Function, Consensus Belief Function, ECSQARU 2009, Suitable Combination Rule