On the Scalability of Evidence Accumulation Clustering

Andre Lourenco, Ana L.N. Fred, Anil K. Jain
2010 2010 20th International Conference on Pattern Recognition  
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.
doi:10.1109/icpr.2010.197 dblp:conf/icpr/LourencoFJ10 fatcat:jshwlfioo5b45jvcmxv5ojsm3q