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.
Khalid Saeed; Rituparna Chaki; Agostino Cortesi; Sławomir Wierzchoń. 12th International Conference on Information Systems and Industrial Management (CISIM), Sep 2013, Krakow, Poland. Springer, Lecture Notes in Computer Science, LNCS-8104, pp.367-378, 2013, Computer Information Systems and Industrial Management. 〈10.1007/978-3-642-40925-7_34〉
https://hal.inria.fr/hal-01496083
Contributeur : Hal Ifip
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Soumis le : lundi 27 mars 2017 - 11:01:44
Dernière modification le : mardi 28 mars 2017 - 01:07:11
Document(s) archivé(s) le : mercredi 28 juin 2017 - 13:06:41
Przemysław Spurek, Jacek Tabor, Krzysztof Misztal. Weighted Approach to Projective Clustering. Khalid Saeed; Rituparna Chaki; Agostino Cortesi; Sławomir Wierzchoń. 12th International Conference on Information Systems and Industrial Management (CISIM), Sep 2013, Krakow, Poland. Springer, Lecture Notes in Computer Science, LNCS-8104, pp.367-378, 2013, Computer Information Systems and Industrial Management. 〈10.1007/978-3-642-40925-7_34〉. 〈hal-01496083〉
