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» ANONAGGREGATE: Anonymizing Data Using Sequential Aggregation
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ICDM
2009
IEEE
130views Data Mining» more  ICDM 2009»
13 years 5 months ago
Efficient Anonymizations with Enhanced Utility
The k-anonymization method is a commonly used privacy-preserving technique. Previous studies used various measures of utility that aim at enhancing the correlation between the orig...
Jacob Goldberger, Tamir Tassa
KDD
2006
ACM
166views Data Mining» more  KDD 2006»
14 years 8 months ago
Anonymizing sequential releases
An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information...
Ke Wang, Benjamin C. M. Fung
ACMSE
2008
ACM
13 years 9 months ago
Mining frequent sequential patterns with first-occurrence forests
In this paper, a new pattern-growth algorithm is presented to mine frequent sequential patterns using First-Occurrence Forests (FOF). This algorithm uses a simple list of pointers...
Erich Allen Peterson, Peiyi Tang
UAI
2008
13 years 9 months ago
Learning Hidden Markov Models for Regression using Path Aggregation
We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for re...
Keith Noto, Mark Craven
DATAMINE
2002
135views more  DATAMINE 2002»
13 years 7 months ago
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
: Web usage mining, possibly used in conjunction with standard approaches to personalization such as collaborative filtering, can help address some of the shortcomings of these tec...
Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakag...