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» Privacy and Data Mining: New Developments and Challenges
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DIS
2004
Springer
14 years 1 months ago
Mining Noisy Data Streams via a Discriminative Model
The two main challenges typically associated with mining data streams are concept drift and data contamination. To address these challenges, we seek learning techniques and models ...
Fang Chu, Yizhou Wang, Carlo Zaniolo
ICDM
2003
IEEE
112views Data Mining» more  ICDM 2003»
14 years 1 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
BMCBI
2008
135views more  BMCBI 2008»
13 years 7 months ago
Facilitating the development of controlled vocabularies for metabolomics technologies with text mining
Background: Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public r...
Irena Spasic, Daniel Schober, Susanna-Assunta Sans...
NIPS
2007
13 years 9 months ago
Mining Internet-Scale Software Repositories
Large repositories of source code create new challenges and opportunities for statistical machine learning. Here we first develop Sourcerer, an infrastructure for the automated c...
Erik Linstead, Paul Rigor, Sushil Krishna Bajracha...
CORR
2008
Springer
115views Education» more  CORR 2008»
13 years 7 months ago
New probabilistic interest measures for association rules
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for ...
Michael Hahsler, Kurt Hornik