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PAKDD
2009
ACM

Pairwise Constrained Clustering for Sparse and High Dimensional Feature Spaces

10 years 9 months ago
Pairwise Constrained Clustering for Sparse and High Dimensional Feature Spaces
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To address this challenge, we propose two novel semi-supervised clustering methods that incorporate prior knowledge in the form of pairwise cluster membership constraints. In particular, we project high-dimensional data onto a much reduced-dimension subspace, where rough clustering structure defined by the prior knowledge is strengthened. Metric learning is then performed on the subspace to construct more informative pairwise distances. We also propose to propagate constraints locally to improve the informativeness of pairwise distances. When the new methods are evaluated using two real benchmark data sets, they show substantial improvement using only limited prior knowledge.
Su Yan, Hai Wang, Dongwon Lee, C. Lee Giles
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where PAKDD
Authors Su Yan, Hai Wang, Dongwon Lee, C. Lee Giles
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