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SDM
2011
SIAM
243views Data Mining» more  SDM 2011»
13 years 24 days ago
Data Integration via Constrained Clustering: An Application to Enzyme Clustering
When multiple data sources are available for clustering, an a priori data integration process is usually required. This process may be costly and may not lead to good clusterings,...
Elisa Boari de Lima, Raquel Cardoso de Melo Minard...
IDA
2011
Springer
13 years 4 months ago
PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments
Abstract. The goal of this article is to introduce a collaborative clustering approach to the domain of ubiquitous knowledge discovery. This clustering approach is suitable in peer...
Benoît Depaire, Rafael Falcón, Koen V...
CVPR
2011
IEEE
13 years 1 months ago
Max-margin Clustering: Detecting Margins from Projections of Points on Lines
Given a unlabelled set of points X ∈ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We...
Raghuraman Gopalan, Jagan Sankaranarayanan
ICASSP
2011
IEEE
13 years 1 months ago
Outlier-aware robust clustering
Clustering is a basic task in a variety of machine learning applications. Partitioning a set of input vectors into compact, wellseparated subsets can be severely affected by the p...
Pedro A. Forero, Vassilis Kekatos, Georgios B. Gia...
JMLR
2011
133views more  JMLR 2011»
13 years 5 months ago
Operator Norm Convergence of Spectral Clustering on Level Sets
Following Hartigan (1975), a cluster is defined as a connected component of the t-level set of the underlying density, that is, the set of points for which the density is greater...
Bruno Pelletier, Pierre Pudlo