Weighted Approach to Projective Clustering

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• 作者：Przemys?aw Spurek ; Jacek Tabor ; Krzysztof Misztal
• 关键词：Projective clustering ; Karhunen ; Loéve Transform ; PCA ; k ; means
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8104
• 期：1
• 页码：379-388
• 全文大小：534KB
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• 作者单位：Przemys?aw Spurek (20)
  Jacek Tabor (20)
  Krzysztof Misztal (21)
  
  20. Faculty of Mathematics and Computer Science, Jagiellonian University, ?ojasiewicza 6, 30-348, Kraków, Poland
  21. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Kraków, Poland
  
• ISSN：1611-3349

文摘

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.