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Weighted Approach to Projective Clustering
Abstract : k-means is the basic method applied in many data clustering problems. As is known, its natural modification can be applied to projection clustering by changing the cost function from the squared-distance from the point to the squared distance from the affine subspace. However, to apply thus approach we need the beforehand knowledge of the dimension.In this paper we show how to modify this approach to allow greater flexibility by using the weights over respective range of subspaces.
Cited literature [12 references]
https://hal.inria.fr/hal-01496083
Contributor : Hal Ifip <>
Submitted on : Monday, March 27, 2017 - 11:01:44 AM
Last modification on : Thursday, October 4, 2018 - 10:12:02 PM
Long-term archiving on: : Wednesday, June 28, 2017 - 1:06:41 PM
Contributor : Hal Ifip <>
Submitted on : Monday, March 27, 2017 - 11:01:44 AM
Last modification on : Thursday, October 4, 2018 - 10:12:02 PM
Long-term archiving on: : Wednesday, June 28, 2017 - 1:06:41 PM
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