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» Discovering frequent patterns in sensitive data
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PPOPP
2005
ACM
14 years 1 months ago
A sampling-based framework for parallel data mining
The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data minin...
Shengnan Cong, Jiawei Han, Jay Hoeflinger, David A...
PODS
2009
ACM
134views Database» more  PODS 2009»
14 years 8 months ago
An efficient rigorous approach for identifying statistically significant frequent itemsets
As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is b...
Adam Kirsch, Michael Mitzenmacher, Andrea Pietraca...
TKDE
2011
186views more  TKDE 2011»
13 years 2 months ago
Discovering Activities to Recognize and Track in a Smart Environment
—The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals expe...
Parisa Rashidi, Diane J. Cook, Lawrence B. Holder,...
GRC
2010
IEEE
13 years 8 months ago
Local Pattern Mining from Sequences Using Rough Set Theory
Abstract--Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns ...
Ken Kaneiwa, Yasuo Kudo
ICDM
2008
IEEE
127views Data Mining» more  ICDM 2008»
14 years 1 months ago
Word Sense Discovery for Web Information Retrieval
Word meaning disambiguation has always been an important problem in many computer science tasks, such as information retrieval and extraction. One of the problems, faced in automa...
Tomasz Nykiel, Henryk Rybinski