# Kernel based K-means clustering using rough set

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##### Citations

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14 citations

### Cites background or methods from "Kernel based K-means clustering usi..."

...Again, it was shown in [20, 21] and [17, 18] respectively that the kernel versions KFCM and KRCM perform better than their normal counterparts....

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...Here, N is total number of data objects [17]....

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...Replacing the Euclidean distance used in the above algorithms for computing the distance by kernel functions some algorithms have been put forth like the kernel based fuzzy c-means (KFCM) [20] and the kernel based rough c-means (KRCM) [17, 18, 21] were introduced Tripathy and Ghosh....

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12 citations

### Cites methods from "Kernel based K-means clustering usi..."

...But using this, the clusters generated are linearly separable....

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11 citations

### Additional excerpts

...[7]-[8]....

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7 citations

### Cites methods from "Kernel based K-means clustering usi..."

...Based on the pseudo flow resistance of one single layer, using the K-means clustering method (Wang and Niu 2004; Kong et al. 2004), layer regrouping is carried out to obtain the optimal production performance....

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6 citations

##### References

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### "Kernel based K-means clustering usi..." refers methods in this paper

...After rough set theory has been proposed by Pawlak [2], we have many clustering algorithms based on it which can handle uncertainty and heterogeneous data and Rough based K-means is one of them....

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