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PKDD
1999
Springer
106views Data Mining» more  PKDD 1999»
15 years 2 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
WSDM
2012
ACM
304views Data Mining» more  WSDM 2012»
13 years 5 months ago
Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial and temporal similarities
Studying relationships between keyword tags on social sharing websites has become a popular topic of research, both to improve tag suggestion systems and to discover connections b...
Haipeng Zhang, Mohammed Korayem, Erkang You, David...
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
15 years 10 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
KDD
2009
ACM
174views Data Mining» more  KDD 2009»
15 years 10 months ago
Audience selection for on-line brand advertising: privacy-friendly social network targeting
This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding ...
Foster J. Provost, Brian Dalessandro, Rod Hook, Xi...
KDD
2008
ACM
217views Data Mining» more  KDD 2008»
15 years 10 months ago
Stream prediction using a generative model based on frequent episodes in event sequences
This paper presents a new algorithm for sequence prediction over long categorical event streams. The input to the algorithm is a set of target event types whose occurrences we wis...
Srivatsan Laxman, Vikram Tankasali, Ryen W. White