# Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function

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34 citations

### Cites background from "Improved rough k-means clustering a..."

...Reflecting the differing impacts of different objects within the same approximation set, a RKM clustering based on a weighted distance measure with Gaussian function was proposed in [19]....

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..., c-means clustering algorithm in some literature [3], [7], [9]–[16]) is still a topic of great interest to researchers, and has attained great popularity [1], [6], [17]–[19]....

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...Considering that the lower approximation set or the boundary maybe empty in some cases, Peters [19] improved the above center iterative calculation formula....

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...Many researchers have made successive improvements by incorporating inter alia fuzzy set, probabilistic model, kernel methods on [4]–[7], [17], [19], [21], [22]....

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33 citations

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### "Improved rough k-means clustering a..." refers methods in this paper

...The rough set theory proposed by Pawlak and Skowron [23] is an important tool to deal with imprecise, incomplete and inconsistent data....

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...Rough set theory, as an important soft computing approach proposed by Pawlak and Skowron [23] for uncertain and vague data analysis, has been shown to be more promising and has been successfully incorporated in the k-means clustering framework by Lingras to develop the rough k-means (RKM) algorithm [9,14]....

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