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» Providing k-anonymity in data mining
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SISAP
2008
IEEE
188views Data Mining» more  SISAP 2008»
15 years 4 months ago
High-Dimensional Similarity Retrieval Using Dimensional Choice
There are several pieces of information that can be utilized in order to improve the efficiency of similarity searches on high-dimensional data. The most commonly used information...
Dave Tahmoush, Hanan Samet
ICDM
2003
IEEE
112views Data Mining» more  ICDM 2003»
15 years 3 months ago
Privacy-preserving Distributed Clustering using Generative Models
We present a framework for clustering distributed data in unsupervised and semi-supervised scenarios, taking into account privacy requirements and communication costs. Rather than...
Srujana Merugu, Joydeep Ghosh
AUSDM
2008
Springer
188views Data Mining» more  AUSDM 2008»
15 years 10 days ago
Priority Driven K-Anonymisation for Privacy Protection
Given the threat of re-identification in our growing digital society, guaranteeing privacy while providing worthwhile data for knowledge discovery has become a difficult problem. ...
Xiaoxun Sun, Hua Wang, Jiuyong Li
CIKM
2010
Springer
14 years 9 months ago
Partial drift detection using a rule induction framework
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...
Damon Sotoudeh, Aijun An
KDD
2006
ACM
173views Data Mining» more  KDD 2006»
15 years 10 months ago
Robust information-theoretic clustering
How do we find a natural clustering of a real world point set, which contains an unknown number of clusters with different shapes, and which may be contaminated by noise? Most clu...
Christian Böhm, Christos Faloutsos, Claudia P...