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» A framework for mining interesting pattern sets
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JCP
2008
104views more  JCP 2008»
13 years 4 months ago
Discovery of Sequential Patterns Coinciding with Analysts' Interests
This paper proposes a new sequential pattern mining method. The method introduces a new evaluation criterion satisfying the Apriori property. The criterion is calculated by the fre...
Shigeaki Sakurai, Youichi Kitahara, Ryohei Orihara...
EDBT
2008
ACM
138views Database» more  EDBT 2008»
14 years 4 months ago
Mine your own business, mine others' news!
Major media companies such as The Financial Times, the Wall Street Journal or Reuters generate huge amounts of textual news data on a daily basis. Mining frequent patterns in this...
Boualem Benatallah, Guillaume Raschia, Noureddine ...
TKDE
2008
156views more  TKDE 2008»
13 years 4 months ago
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns ident...
Yan Huang, Liqin Zhang, Pusheng Zhang
ICDM
2006
IEEE
149views Data Mining» more  ICDM 2006»
13 years 10 months ago
Pattern Mining in Frequent Dynamic Subgraphs
Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowl...
Karsten M. Borgwardt, Hans-Peter Kriegel, Peter Wa...
CORR
2008
Springer
115views Education» more  CORR 2008»
13 years 4 months ago
New probabilistic interest measures for association rules
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for ...
Michael Hahsler, Kurt Hornik