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» Mining Very Large Databases
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165
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BMCBI
2005
163views more  BMCBI 2005»
15 years 3 months ago
Alkahest NuclearBLAST: a user-friendly BLAST management and analysis system
Background -: Sequencing of EST and BAC end datasets is no longer limited to large research groups. Drops in per-base pricing have made high throughput sequencing accessible to in...
Stephen E. Diener, Thomas D. Houfek, Sam E. Kalat,...
196
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VLDB
2002
ACM
101views Database» more  VLDB 2002»
16 years 3 months ago
Efficient similarity search for market basket data
Several organizations have developed very large market basket databases for the maintenance of customer transactions. New applications, e.g., Web recommendation systems, present th...
Alexandros Nanopoulos, Yannis Manolopoulos
142
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KDD
2012
ACM
178views Data Mining» more  KDD 2012»
13 years 6 months ago
Differentially private transit data publication: a case study on the montreal transportation system
With the wide deployment of smart card automated fare collection (SCAFC) systems, public transit agencies have been benefiting from huge volume of transit data, a kind of sequent...
Rui Chen, Benjamin C. M. Fung, Bipin C. Desai, N&e...
150
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SDM
2004
SIAM
225views Data Mining» more  SDM 2004»
15 years 4 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
129
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AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
15 years 9 months ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh