Eigenvalue-based Incremental Spectral Clustering
Mieczysław A. Kłopotek Bartłmiej Starosta Sławomir T. Wierzchoń
Our previous experiments demonstrated that subsets collections of (short)
documents (with several hundred entries) share a common normalized in some way
eigenvalue spectrum of combinatorial Laplacian. Based on this insight, we
propose a method of incremental spectral clustering. The method consists of the
following steps: (1) split the data into manageable subsets, (2) cluster each
of the subsets, (3) merge clusters from different subsets based on the
eigenvalue spectrum similarity to form clusters of the entire set. This method
can be especially useful for clustering methods of complexity strongly
increasing with the size of the data sample,like in case of typical spectral
clustering. Experiments were performed showing that in fact the clustering and
merging the subsets yields clusters close to clustering the entire dataset.