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» Closed Sets for Labeled Data
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KDD
2006
ACM
160views Data Mining» more  KDD 2006»
14 years 10 months ago
Coherent closed quasi-clique discovery from large dense graph databases
Frequent coherent subgraphscan provide valuable knowledgeabout the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large...
Zhiping Zeng, Jianyong Wang, Lizhu Zhou, George Ka...
IPPS
2008
IEEE
14 years 4 months ago
Parallel mining of closed quasi-cliques
Graph structure can model the relationships among a set of objects. Mining quasi-clique patterns from large dense graph data makes sense with respect to both statistic and applica...
Yuzhou Zhang, Jianyong Wang, Zhiping Zeng, Lizhu Z...
ICDE
2007
IEEE
1577views Database» more  ICDE 2007»
15 years 10 months ago
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to...
Ninghui Li, Tiancheng Li, Suresh Venkatasubramania...
DAGM
2004
Springer
14 years 3 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
ACL
2006
13 years 11 months ago
Boosting Statistical Word Alignment Using Labeled and Unlabeled Data
This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach ...
Hua Wu, Haifeng Wang, Zhan-yi Liu