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    » Agent-Enriched Data Mining Using an Extendable Framework
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    SDM
    2008
    SIAM
    123views Data Mining» more  SDM 2008»
    Constrained Co-clustering of Gene Expression Data
    13 years 10 months ago
    Constrained Co-clustering of Gene Expression Data
    Download  siam.org
    In many applications, the expert interpretation of coclustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a collection...
    Ruggero G. Pensa, Jean-François Boulicaut
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    WPES
    2003
    ACM
    161views Security Privacy» more  WPES 2003»
    Analysis of privacy preserving random perturbation techniques: further explorations
    14 years 2 months ago
    Analysis of privacy preserving random perturbation techniques: further explorations
    Download  www.eecs.wsu.edu
    Privacy is becoming an increasingly important issue in many data mining applications, particularly in the security and defense area. This has triggered the development of many pri...
    Haimonti Dutta, Hillol Kargupta, Souptik Datta, Kr...
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    GPEM
    2007
    119views more  GPEM 2007»
    Genomic mining for complex disease traits with "random chemistry"
    13 years 8 months ago
    Genomic mining for complex disease traits with "random chemistry"
    Download  www.emba.uvm.edu
    Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could revolut...
    Margaret J. Eppstein, Joshua L. Payne, Bill C. Whi...
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    SDM
    2007
    SIAM
    137views Data Mining» more  SDM 2007»
    Semi-supervised Feature Selection via Spectral Analysis
    13 years 10 months ago
    Semi-supervised Feature Selection via Spectral Analysis
    Download  www.public.asu.edu
    Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
    Zheng Zhao, Huan Liu
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    SDM
    2010
    SIAM
    144views Data Mining» more  SDM 2010»
    A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
    13 years 10 months ago
    A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
    Download  www.cs.cmu.edu
    This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
    Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
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