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» Mining Very Large Databases
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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
15 years 10 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
SIGMOD
1999
ACM
183views Database» more  SIGMOD 1999»
15 years 2 months ago
OPTICS: Ordering Points To Identify the Clustering Structure
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysi...
Mihael Ankerst, Markus M. Breunig, Hans-Peter Krie...
KDD
2004
ACM
134views Data Mining» more  KDD 2004»
15 years 10 months ago
Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pair
Given a user-specified minimum correlation threshold and a market basket database with N items and T transactions, an all-strong-pairs correlation query finds all item pairs with...
Hui Xiong, Shashi Shekhar, Pang-Ning Tan, Vipin Ku...
EDBT
2012
ACM
257views Database» more  EDBT 2012»
13 years 6 days ago
Indexing and mining topological patterns for drug discovery
Increased availability of large repositories of chemical compounds has created new challenges and opportunities for the application of data-mining and indexing techniques to probl...
Sayan Ranu, Ambuj K. Singh
CIKM
2009
Springer
15 years 4 months ago
Mining tourist information from user-supplied collections
Tourist photographs constitute a large part of the images uploaded to photo sharing platforms. But filtering methods are needed before one can extract useful knowledge from noisy ...
Adrian Popescu, Gregory Grefenstette, Pierre-Alain...