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» CUM: An Efficient Framework for Mining Concept Units
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SIGKDD
2010
125views more  SIGKDD 2010»
13 years 1 months ago
A framework for mining interesting pattern sets
This paper suggests a framework for mining subjectively interesting pattern sets that is based on two components: (1) the encoding of prior information in a model for the data min...
Tijl De Bie, Kleanthis-Nikolaos Kontonasios, Eirin...
WWW
2009
ACM
14 years 7 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...
CAEPIA
2003
Springer
13 years 11 months ago
Text Mining Using the Hierarchical Syntactical Structure of Documents
One of the most important tasks for determining association rules consists of calculating all the maximal frequent itemsets. Specifically, some methods to obtain these itemsets hav...
Roxana Dánger, José Ruiz-Shulcloper,...
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
14 years 6 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
ASIAMS
2007
IEEE
14 years 18 days ago
Rough-Fuzzy Granulation, Rough Entropy and Image Segmentation
This talk has two parts explaining the significance of Rough sets in granular computing in terms of rough set rules and in uncertainty handling in terms of lower and upper approxi...
Sankar K. Pal