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» Probing Knowledge in Distributed Data Mining
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ICDM
2007
IEEE
149views Data Mining» more  ICDM 2007»
14 years 1 months ago
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...
Tao Li, Chris H. Q. Ding, Michael I. Jordan
ICDM
2007
IEEE
184views Data Mining» more  ICDM 2007»
14 years 1 months ago
Bayesian Folding-In with Dirichlet Kernels for PLSI
Probabilistic latent semantic indexing (PLSI) represents documents of a collection as mixture proportions of latent topics, which are learned from the collection by an expectation...
Alexander Hinneburg, Hans-Henning Gabriel, Andr&eg...
INFOSCALE
2006
ACM
14 years 1 months ago
PENS: an algorithm for density-based clustering in peer-to-peer systems
Huge amounts of data are available in large-scale networks of autonomous data sources dispersed over a wide area. Data mining is an essential technology for obtaining hidden and v...
Mei Li, Guanling Lee, Wang-Chien Lee, Anand Sivasu...
KDD
1997
ACM
154views Data Mining» more  KDD 1997»
13 years 11 months ago
Autonomous Discovery of Reliable Exception Rules
This paper presents an autonomous algorithm for discovering exception rules from data sets. An exception rule, which is defined as a deviational pattern to a well-known fact, exhi...
Einoshin Suzuki
ICDM
2010
IEEE
200views Data Mining» more  ICDM 2010»
13 years 5 months ago
Bayesian Maximum Margin Clustering
Abstract--Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered...
Bo Dai, Baogang Hu, Gang Niu