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KDD
2010
ACM
279views Data Mining» more  KDD 2010»
14 years 19 days ago
Unifying dependent clustering and disparate clustering for non-homogeneous data
Modern data mining settings involve a combination of attributevalued descriptors over entities as well as specified relationships between these entities. We present an approach t...
M. Shahriar Hossain, Satish Tadepalli, Layne T. Wa...
SDM
2007
SIAM
112views Data Mining» more  SDM 2007»
13 years 10 months ago
PoClustering: Lossless Clustering of Dissimilarity Data
Given a set of objects V with a dissimilarity measure between pairs of objects in V , a PoCluster is a collection of sets P ⊂ powerset(V ) partially ordered by the ⊂ relation ...
Jinze Liu, Qi Zhang, Wei Wang 0010, Leonard McMill...
SIGMOD
2011
ACM
269views Database» more  SIGMOD 2011»
12 years 11 months ago
Advancing data clustering via projective clustering ensembles
Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering ensembles and projective clustering...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
ICA
2004
Springer
14 years 2 months ago
Soft-LOST: EM on a Mixture of Oriented Lines
Robust clustering of data into overlapping linear subspaces is a common problem. Here we consider one-dimensional subspaces that cross the origin. This problem arises in blind sour...
Paul D. O'Grady, Barak A. Pearlmutter
ICDM
2006
IEEE
100views Data Mining» more  ICDM 2006»
14 years 2 months ago
Meta Clustering
Clustering is ill-defined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the cl...
Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, ...