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ICASSP
2011
IEEE
14 years 6 months ago
Bayesian Compressive Sensing for clustered sparse signals
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
Lei Yu, Hong Sun, Jean-Pierre Barbot, Gang Zheng
EUROPAR
1999
Springer
15 years 7 months ago
Parallel k/h-Means Clustering for Large Data Sets
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We ...
Kilian Stoffel, Abdelkader Belkoniene
ICDM
2005
IEEE
138views Data Mining» more  ICDM 2005»
15 years 8 months ago
Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values
Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have t...
Hung-Leng Chen, Kun-Ta Chuang, Ming-Syan Chen
IPM
2006
88views more  IPM 2006»
15 years 3 months ago
Testing the cluster hypothesis in distributed information retrieval
How to merge and organise query results retrieved from different resources is one of the key issues in distributed information retrieval. Some previous research and experiments su...
Fabio Crestani, Shengli Wu
IPM
2007
158views more  IPM 2007»
15 years 2 months ago
Validation and interpretation of Web users' sessions clusters
Understanding users’ navigation on the Web is important towards improving the quality of information and the speed of accessing large-scale Web data sources. Clustering of users...
George Pallis, Lefteris Angelis, Athena Vakali