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» Efficient Discovery of Confounders in Large Data Sets
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WWW
2009
ACM
14 years 8 months ago
Smart Miner: a new framework for mining large scale web usage data
In this paper, we propose a novel framework called SmartMiner for web usage mining problem which uses link information for producing accurate user sessions and frequent navigation...
Murat Ali Bayir, Ismail Hakki Toroslu, Ahmet Cosar...
SIGMOD
2002
ACM
132views Database» more  SIGMOD 2002»
14 years 7 months ago
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of th...
Haixun Wang, Wei Wang 0010, Jiong Yang, Philip S. ...
ACSAC
2001
IEEE
13 years 11 months ago
Mining Alarm Clusters to Improve Alarm Handling Efficiency
It is a well-known problem that intrusion detection systems overload their human operators by triggering thousands of alarms per day. As a matter of fact, we have been asked by on...
Klaus Julisch
FGR
2004
IEEE
133views Biometrics» more  FGR 2004»
13 years 11 months ago
Finding Temporal Patterns by Data Decomposition
We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find stru...
David C. Minnen, Christopher Richard Wren
ISCI
2008
116views more  ISCI 2008»
13 years 7 months ago
Discovery of maximum length frequent itemsets
The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often ...
Tianming Hu, Sam Yuan Sung, Hui Xiong, Qian Fu