On the Scalability of Evidence Accumulation Clustering

Abstract—This work focuses on the scalability of the Evidence Accumulation Clustering (EAC) method. We first address the space complexity of the co-association matrix. The sparseness of the matrix is related to the construction of the clustering ensemble. Using a split and merge strategy combined with a sparse matrix representation, we empirically show that a linear space complexity is achievable in this framework, leading to the scalability of EAC method to clustering large data-sets. Keywords-Cluster analysis; combining clustering partitions; cluster fusion, evidence accumulation; large data-sets.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: André Lourenço, Ana Luisa Nobre Fred, Anil Jain
• Keywords: Computer Vision, Evidence Accumulation, ICPR 2010, Large Data-sets, Space Complexity