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» Efficient frequent pattern mining over data streams
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ICDM
2009
IEEE
167views Data Mining» more  ICDM 2009»
14 years 7 months ago
Self-Adaptive Anytime Stream Clustering
Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
Philipp Kranen, Ira Assent, Corinna Baldauf, Thoma...
IDEAS
2000
IEEE
90views Database» more  IDEAS 2000»
15 years 2 months ago
StreamJoin: A Generic Database Approach to Support the Class of Stream-Oriented Applications
Today many applications routinely generate large quantities of data. The data often takes the form of (time) series, or more generally streams, i.e. an ordered sequence of records...
Clara Nippl, Ralf Rantzau, Bernhard Mitschang
KDD
2005
ACM
151views Data Mining» more  KDD 2005»
15 years 10 months ago
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has man...
Qiaozhu Mei, ChengXiang Zhai
KDD
2006
ACM
123views Data Mining» more  KDD 2006»
15 years 10 months ago
Mining rank-correlated sets of numerical attributes
We study the mining of interesting patterns in the presence of numerical attributes. Instead of the usual discretization methods, we propose the use of rank based measures to scor...
Toon Calders, Bart Goethals, Szymon Jaroszewicz
WWW
2008
ACM
14 years 9 months ago
Discovering geographical-specific interests from web click data
As the Internet continues to play an important role in many business applications, it becomes vital to increase the competitive edge by offering geographically tailored contents t...
Chang Sheng, Wynne Hsu, Mong-Li Lee