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» Discovering Informative Patterns and Data Cleaning
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MSR
2010
ACM
14 years 14 days ago
Validity of network analyses in Open Source Projects
—Social network methods are frequently used to analyze networks derived from Open Source Project communication and collaboration data. Such studies typically discover patterns in...
Roozbeh Nia, Christian Bird, Premkumar T. Devanbu,...
PODS
2009
ACM
134views Database» more  PODS 2009»
14 years 8 months ago
An efficient rigorous approach for identifying statistically significant frequent itemsets
As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is b...
Adam Kirsch, Michael Mitzenmacher, Andrea Pietraca...
PR
2006
78views more  PR 2006»
13 years 7 months ago
Similarity-based analysis for large networks of ultra-low resolution sensors
By analyzing the similarities between bit streams coming from a network of motion detectors, we can recover the network geometry and discover structure in the human behavior being...
Christopher Richard Wren, David C. Minnen, Sriniva...
PKDD
1999
Springer
106views Data Mining» more  PKDD 1999»
13 years 11 months ago
Heuristic Measures of Interestingness
When mining a large database, the number of patterns discovered can easily exceed the capabilities of a human user to identify interesting results. To address this problem, variou...
Robert J. Hilderman, Howard J. Hamilton
SAC
2009
ACM
14 years 2 months ago
Applying latent dirichlet allocation to group discovery in large graphs
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
Keith Henderson, Tina Eliassi-Rad