@inproceedings{garg_pbirch_2006,
	title = {{PBIRCH}: {A} {Scalable} {Parallel} {Clustering} algorithm for {Incremental} {Data}},
	shorttitle = {{PBIRCH}},
	doi = {10.1109/IDEAS.2006.36},
	abstract = {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},
	booktitle = {2006 10th {International} {Database} {Engineering} and {Applications} {Symposium} ({IDEAS}'06)},
	author = {Garg, Ashwani and Mangla, Ashish and Gupta, Neelima and Bhatnagar, Vasudha},
	month = dec,
	year = {2006},
	note = {ISSN: 1098-8068},
	keywords = {Algorithm design and analysis, Broadcasting, Clustering algorithms, Computer science, Delay, Memory management, Message passing, Partitioning algorithms, Scalability, Time factors},
	pages = {315--316},
}

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