@ARTICLE{Tsoumakas_04b ,
    AUTHOR      = {Grigorios Tsoumakas and Lefteris Angelis and Ioannis Vlahavas},
    TITLE       = {{Clustering Classifiers for Knowledge Discovery from Physically Distributed Databases}},
    JOURNAL     = {{Data and Knowledge Engineering}},
    YEAR        = {2004},
    VOLUME      = {49},
    NUMBER      = {3},
    PAGES       = {223--242},
    MONTH       = {June},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://portal.acm.org/citation.cfm?id=1011061},
    ABSTRACT    = {Most distributed classification approaches view data distribution as a technical
    issue and combine local models aiming at a single global model. This however, is unsuitable for
    inherently distributed databases, which are often described by more than one classification models
    that might differ conceptually. In this paper we present an approach for clustering distributed
    classifiers in order to discover groups of similar classifiers and thus similar databases with
    respect to a specific classification task. We also show that clustering distributed classifiers
    as a pre-processing step for classifier combination enhances the achieved predictive performance
    of the ensemble.
},
}