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KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 7 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
SDM
2009
SIAM
152views Data Mining» more  SDM 2009»
16 years 3 months ago
Multiple Kernel Clustering.
Maximum margin clustering (MMC) has recently attracted considerable interests in both the data mining and machine learning communities. It first projects data samples to a kernel...
Bin Zhao, James T. Kwok, Changshui Zhang
PKDD
2009
Springer
120views Data Mining» more  PKDD 2009»
16 years 1 months ago
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn fea...
Shuang-Hong Yang, Hongyuan Zha, Shaohua Kevin Zhou...
SISAP
2008
IEEE
175views Data Mining» more  SISAP 2008»
16 years 1 months ago
Anytime K-Nearest Neighbor Search for Database Applications
Many contemporary database applications require similarity-based retrieval of complex objects where the only usable knowledge of its domain is determined by a metric distance func...
Weijia Xu, Daniel P. Miranker, Rui Mao, Smriti R. ...
ICDM
2007
IEEE
129views Data Mining» more  ICDM 2007»
16 years 28 days ago
A Generalization of Proximity Functions for K-Means
K-means is a widely used partitional clustering method. A large amount of effort has been made on finding better proximity (distance) functions for K-means. However, the common c...
Junjie Wu, Hui Xiong, Jian Chen, Wenjun Zhou