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» Techniques of Cluster Algorithms in Data Mining
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DAWAK
2006
Springer
15 years 10 months ago
Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures
Abstract. Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view ...
Jiuyong Li, Raymond Chi-Wing Wong, Ada Wai-Chee Fu...
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
16 years 1 months ago
Set-Based Boosting for Instance-Level Transfer
—The success of transfer to improve learning on a target task is highly dependent on the selected source data. Instance-based transfer methods reuse data from the source tasks to...
Eric Eaton, Marie desJardins
AUSDM
2007
Springer
94views Data Mining» more  AUSDM 2007»
15 years 10 months ago
Classification for accuracy and insight: A weighted sum approach
This research presents a classifier that aims to provide insight into a dataset in addition to achieving classification accuracies comparable to other algorithms. The classifier c...
Anthony Quinn, Andrew Stranieri, John Yearwood
KDD
2005
ACM
143views Data Mining» more  KDD 2005»
16 years 6 months ago
SVM selective sampling for ranking with application to data retrieval
Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vect...
Hwanjo Yu
BMCBI
2007
177views more  BMCBI 2007»
15 years 6 months ago
The BioPrompt-box: an ontology-based clustering tool for searching in biological databases
Background: High-throughput molecular biology provides new data at an incredible rate, so that the increase in the size of biological databanks is enormous and very rapid. This sc...
Claudio Corsi, Paolo Ferragina, Roberto Marangoni