A generative dyadic aspect model for evidence accumulation clustering
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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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.