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KDD
2004
ACM
170views Data Mining» more  KDD 2004»
14 years 10 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
KDD
2004
ACM
113views Data Mining» more  KDD 2004»
14 years 10 months ago
Learning spatially variant dissimilarity (SVaD) measures
Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure betwee...
Krishna Kummamuru, Raghu Krishnapuram, Rakesh Agra...
KDD
2004
ACM
164views Data Mining» more  KDD 2004»
14 years 10 months ago
Cluster-based concept invention for statistical relational learning
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Clustering improves...
Alexandrin Popescul, Lyle H. Ungar
KDD
2004
ACM
147views Data Mining» more  KDD 2004»
14 years 3 months ago
Semantic representation: search and mining of multimedia content
Semantic understanding of multimedia content is critical in enabling effective access to all forms of digital media data. By making large media repositories searchable, semantic ...
Apostol Natsev, Milind R. Naphade, John R. Smith
KDD
2004
ACM
159views Data Mining» more  KDD 2004»
14 years 3 months ago
Optimal randomization for privacy preserving data mining
Randomization is an economical and efficient approach for privacy preserving data mining (PPDM). In order to guarantee the performance of data mining and the protection of individ...
Michael Yu Zhu, Lei Liu