Distributed Clustering Based on K-Means and CPGA

Distributed clustering is a new research field of data mining now. In this paper, one of distributed clustering named DCBKC (distributed clustering based on K-means and coarse-grained parallel genetic algorithm) based on K-means and coarse-grained parallel genetic algorithm is advanced. The algorithm can solve local clustering problem of distributed clustering effectively, reflect all of local data characters, enhance local datapsilas perspectivity and decrease network overload at a way by adopting proper migration strategy simultaneously. Both theory analysis and experimental results confirm that DCBKC is feasible.