A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
On the Scalability of Evidence Accumulation Clustering
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