Spectral aggregation for clustering ensemble

Since a large number of clustering algorithms exist, aggregating different clustered partitions into a single consolidated one to obtain better results has become an important problem. We propose a new algorithm for clustering ensemble based on spectral clustering. We also propose a criteria along with this algorithm, for the detection of cluster numbers. Our algorithm can determine the number of clusters more accurately with less volatility, and therefore can deduce a better combined clustering result. Experimental results on both synthesis and real data-sets show the capability and robustness of our approach.