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Improved rough k -means clustering algorithm based on weighted distance measure with Gaussian function.
- Source :
-
International Journal of Computer Mathematics . Apr2017, Vol. 94 Issue 4, p663-675. 13p. - Publication Year :
- 2017
-
Abstract
- Roughk-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the roughk-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved roughk-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00207160
- Volume :
- 94
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- International Journal of Computer Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 121307787
- Full Text :
- https://doi.org/10.1080/00207160.2015.1124099