White Papers
Parallel Spectral Clustering Algorithm for Large-Scale Community Data Mining
Category: Data Management, Servers and Server OS
Overview 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, the author parallelizes the algorithm by dividing both memory use and computation on distributed machines. Empirical study on some small datasets shows the accuracy of the parallelization scheme. Empirical study on a large community dataset obtained from Orkut demonstrates the scalability of the parallel spectral clustering algorithm.
- Publisher
- University of California
- File Format
- Date Published
- Oct 1, 2008
- Format
- White Papers
- Topics
- Parallel Processing, Data Mining - Analysis
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