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Abstract : |
In this paper, we present a bottom-up clustering algorithm based on recursive collapsing of small cliques in a graph. The sizes of the small cliques are derived using random graph theory. This clustering algorithm leads to a natural parallel implementation in which multiple processors are used to identify clusters simultaneously. We also present a cluster-based partitioning method in which our clustering algorithm is used as a preprocessing step to both the bisection algorithm by Fiduccia and Mattheyses and a ratio-cut algorithm by Wei and Cheng. Our results show that cluster-based partitioning obtains cut sizes up to 49.6 % smaller than the bisection algorithm, and obtains ratio cut sizes up to 66.8 % smaller than the ratio-cut algorithm. Moreover, we show that cluster-based partitioning produces much stabler results than direct partitioning., |
