%0 Conference Proceedings
%T Weighted Approach to Projective Clustering
%+ Faculty of Mathematics and Computer Science of the Jagiellonian University
%+ Faculty of Physics and Applied Computer Science [Kraków] (FPACS)
%A Spurek, Przemysław
%A Tabor, Jacek
%A Misztal, Krzysztof
%Z Part 7: Algorithms
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 12th International Conference on Information Systems and Industrial Management (CISIM)
%C Krakow, Poland
%Y Khalid Saeed
%Y Rituparna Chaki
%Y Agostino Cortesi
%Y Sławomir Wierzchoń
%I Springer
%3 Computer Information Systems and Industrial Management
%V LNCS-8104
%P 367-378
%8 2013-09-25
%D 2013
%R 10.1007/978-3-642-40925-7_34
%K Projective clustering
%K Karhunen-Loéve Transform
%K PCA
%K k-means
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01496083/document
%2 https://inria.hal.science/hal-01496083/file/978-3-642-40925-7_34_Chapter.pdf
%L hal-01496083
%U https://inria.hal.science/hal-01496083
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-8104