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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
ICDM
2005
IEEE
139views Data Mining» more  ICDM 2005»
14 years 1 months ago
Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation
In order to generate synthetic basket data sets for better benchmark testing, it is important to integrate characteristics from real-life databases into the synthetic basket data ...
Yongge Wang, Xintao Wu
KDD
2004
ACM
160views Data Mining» more  KDD 2004»
14 years 8 months ago
k-TTP: a new privacy model for large-scale distributed environments
Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a gen...
Bobi Gilburd, Assaf Schuster, Ran Wolff
DKE
2008
159views more  DKE 2008»
13 years 7 months ago
Isolated items discarding strategy for discovering high utility itemsets
Traditional methods of association rule mining consider the appearance of an item in a transaction, whether or not it is purchased, as a binary variable. However, customers may pu...
Yu-Chiang Li, Jieh-Shan Yeh, Chin-Chen Chang
KDD
2005
ACM
89views Data Mining» more  KDD 2005»
14 years 8 months ago
Mining risk patterns in medical data
In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemi...
Jiuyong Li, Ada Wai-Chee Fu, Hongxing He, Jie Chen...