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» Evaluation of Sampling for Data Mining of Association Rules
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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
15 years 7 months ago
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
Tobias Jung, Peter Stone
107
Voted
SDM
2009
SIAM
123views Data Mining» more  SDM 2009»
15 years 10 months ago
Randomization Techniques for Graphs.
Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those resu...
Gemma C. Garriga, Kai Puolamäki, Sami Hanhij&...
105
Voted
IFIP12
2008
15 years 2 months ago
Agent Based Frequent Set Meta Mining: Introducing EMADS
In this paper we: introduce EMADS, the Extendible Multi-Agent Data mining System, to support the dynamic creation of communities of data mining agents; explore the capabilities of ...
Kamal Ali Albashiri, Frans Coenen, Paul H. Leng
PPL
2008
88views more  PPL 2008»
15 years 19 days ago
Evaluating Computational Costs while Handling Data and Control Parallelism
The aim of this work is to introduce a computational costs system associated to a semantic framework for orthogonal data and control parallelism handling. In such a framework a pa...
Sonia Campa
113
Voted
PAKDD
2007
ACM
121views Data Mining» more  PAKDD 2007»
15 years 6 months ago
Two-Phase Algorithms for a Novel Utility-Frequent Mining Model
When companies seek for the combination of products which can constantly generate high profit, the association rule mining (ARM) or the utility mining will not achieve such task. ...
Jieh-Shan Yeh, Yu-Chiang Li, Chin-Chen Chang