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ADC
2003
Springer
182views Database» more  ADC 2003»
15 years 11 months ago
CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expens...
Yudho Giri Sucahyo, Raj P. Gopalan
SACMAT
2005
ACM
15 years 12 months ago
Role mining with ORCA
With continuously growing numbers of applications, enterprises face the problem of efficiently managing the assignment of access permissions to their users. On the one hand, secur...
Jürgen Schlegelmilch, Ulrike Steffens
MOBICOM
2012
ACM
13 years 8 months ago
RadioJockey: mining program execution to optimize cellular radio usage
Many networked applications that run in the background on a mobile device incur significant energy drains when using the cellular radio interface for communication. This is mainl...
Pavan K. Athivarapu, Ranjita Bhagwan, Saikat Guha,...
PAKDD
2010
ACM
173views Data Mining» more  PAKDD 2010»
15 years 4 months ago
Distributed Knowledge Discovery with Non Linear Dimensionality Reduction
Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifol...
Panagis Magdalinos, Michalis Vazirgiannis, Dialect...
PAKDD
2005
ACM
94views Data Mining» more  PAKDD 2005»
15 years 11 months ago
Progressive Sampling for Association Rules Based on Sampling Error Estimation
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...
Kun-Ta Chuang, Ming-Syan Chen, Wen-Chieh Yang