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» Hiding Sensitive Patterns in Association Rules Mining
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ADC
2003
Springer
182views Database» more  ADC 2003»
14 years 26 days 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
KDD
1997
ACM
154views Data Mining» more  KDD 1997»
13 years 11 months ago
Autonomous Discovery of Reliable Exception Rules
This paper presents an autonomous algorithm for discovering exception rules from data sets. An exception rule, which is defined as a deviational pattern to a well-known fact, exhi...
Einoshin Suzuki
HICSS
2006
IEEE
138views Biometrics» more  HICSS 2006»
14 years 1 months ago
An Efficient Algorithm for Real-Time Frequent Pattern Mining for Real-Time Business Intelligence Analytics
Finding frequent patterns from databases has been the most time consuming process in data mining tasks, like association rule mining. Frequent pattern mining in real-time is of in...
Rajanish Dass, Ambuj Mahanti
VLDB
2008
ACM
147views Database» more  VLDB 2008»
14 years 7 months ago
Providing k-anonymity in data mining
In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in...
Arik Friedman, Ran Wolff, Assaf Schuster
MLDM
2007
Springer
14 years 1 months ago
Mining Frequent Trajectories of Moving Objects for Location Prediction
Advances in wireless and mobile technology flood us with amounts of moving object data that preclude all means of manual data processing. The volume of data gathered from position...
Mikolaj Morzy