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TNN
2010
155views Management» more  TNN 2010»
14 years 11 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
JMLR
2012
13 years 7 months ago
Maximum Margin Temporal Clustering
Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on e...
Minh Hoai Nguyen, Fernando De la Torre
IPPS
2003
IEEE
15 years 10 months ago
Parallel ROLAP Data Cube Construction On Shared-Nothing Multiprocessors
The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining tas...
Ying Chen, Frank K. H. A. Dehne, Todd Eavis, Andre...
ICDE
2008
IEEE
166views Database» more  ICDE 2008»
16 years 6 months ago
A Clustered Index Approach to Distributed XPath Processing
Supporting top-k queries over distributed collections of schemaless XML data poses two challenges. While XML supports expressive query languages such as XPath and XQuery, these la...
Georgia Koloniari, Evaggelia Pitoura
ICML
2007
IEEE
16 years 5 months ago
Maximum margin clustering made practical
Maximum margin clustering (MMC) is a recent large margin unsupervised learning approach that has often outperformed conventional clustering methods. Computationally, it involves n...
Kai Zhang, Ivor W. Tsang, James T. Kwok