Lightweight clustering technique for distributed data mining applications
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Evolutionary k-means for distributed data sets
Neurocomputing
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We present a parallel version of BIRCH with the objective of enhancing the scalability without compromising on the quality of clustering. The incoming data is distributed in a cyclic manner (or block cyclic manner if the data is bursty) to balance the load among processors. The algorithm is implemented on a message passing share-nothing model. Experiments show that for very large data sets the algorithm scales nearly linearly with the increasing number of processors. Experiments also show that clusters obtained by PBIRCH are comparable to those obtained using BIRCH.