Parallel Spectral Clustering Algorithm for Large-Scale Community Data Mining

The spectral clustering algorithm has been shown to be very effective in finding clusters of non-linear boundaries. Unfortunately, spectral clustering suffers from the scalability problem in both memory use and computational time. In this work, we parallelize the algorithm by dividing both memory use and computation on distributed machines. Empirical study on some small datasets shows the accuracy of our parallelization scheme. Empirical study on a large community dataset obtained from Orkut demonstrates the scalability of our parallel spectral clustering algorithm.