Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IDEAS.2006.36 | Delhi |
Keywords | Field | DocType |
message passing,parallel algorithms,pattern clustering,resource allocation,very large databases,PBIRCH scalable parallel clustering algorithm,incremental data,load balance,massive dataset clustering,message passing share-nothing model | Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Affinity propagation,Correlation clustering,Computer science,Parallel algorithm,Parallel computing,Constrained clustering,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1098-8068 | 0-7695-2577-6 | 18 |
PageRank | References | Authors |
0.66 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashwani Garg | 1 | 18 | 0.66 |
| Ashish Mangla | 2 | 18 | 0.66 |
| Neelima Gupta | 3 | 159 | 19.69 |
| Vasudha Bhatnagar | 4 | 181 | 17.69 |