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Clustering using a coarse-grained parallel Genetic Algorithm : APreliminary
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nalini, Study Ratha, Kshirod Kumar Jain, Anil K. Moon, Jeongseung Department, Chung |
| Copyright Year | 1995 |
| Abstract | Genetic Algorithms (GA) are useful in solving complex optimization problems. By posing pattern clustering as an optimization problem, GAs can be used to obtain an optimal minimum squared-error partitions. In order to improve the total execution time, a distributed algorithm has been developed using the divide and conquer approach. Using a standard communication library called PVM, the distributed algorithm has been implemented on a workstation cluster. The GA approach gives better quality clusters for many data sets compared to a standard K-Means clustering algorithm. We have achieved a near linear speedup for the distributed implementation. |
| File Format | PDF HTM / HTML |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
