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SDM
2004
SIAM
225views Data Mining» more  SDM 2004»
15 years 3 months ago
Active Semi-Supervision for Pairwise Constrained Clustering
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constra...
Sugato Basu, Arindam Banerjee, Raymond J. Mooney
SDM
2003
SIAM
125views Data Mining» more  SDM 2003»
15 years 3 months ago
Scalable, Balanced Model-based Clustering
This paper presents a general framework for adapting any generative (model-based) clustering algorithm to provide balanced solutions, i.e., clusters of comparable sizes. Partition...
Shi Zhong, Joydeep Ghosh
ICDM
2009
IEEE
175views Data Mining» more  ICDM 2009»
14 years 11 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
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
16 years 2 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
127
Voted
AI
2005
Springer
15 years 7 months ago
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets
Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we ...
Xin Wang, Howard J. Hamilton