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» Approximation schemes for clustering problems
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FOCS
2010
IEEE
14 years 7 months ago
Stability Yields a PTAS for k-Median and k-Means Clustering
We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean spaces, in the setting where k is part of the input (not a constant). For the k-means pr...
Pranjal Awasthi, Avrim Blum, Or Sheffet
PRL
2010
158views more  PRL 2010»
14 years 8 months ago
Data clustering: 50 years beyond K-means
: Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organ...
Anil K. Jain
SDM
2009
SIAM
114views Data Mining» more  SDM 2009»
15 years 7 months ago
GAD: General Activity Detection for Fast Clustering on Large Data.
In this paper, we propose GAD (General Activity Detection) for fast clustering on large scale data. Within this framework we design a set of algorithms for different scenarios: (...
Jiawei Han, Liangliang Cao, Sangkyum Kim, Xin Jin,...
SIGMOD
2011
ACM
269views Database» more  SIGMOD 2011»
14 years 15 days 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...
ICDM
2009
IEEE
175views Data Mining» more  ICDM 2009»
14 years 7 months ago
Maximum Margin Clustering with Multivariate Loss Function
This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clust...
Bin Zhao, James Tin-Yau Kwok, Changshui Zhang