@INPROCEEDINGS{Eisenhardt_03,
    AUTHOR      = {M. Eisenhardt and W. Muller and A. Henrich},
    TITLE       = {{Classifying Documents by Distributed P2P Clustering}},
    BOOKTITLE   = {{Proceedings of Informatik 2003}},
    YEAR        = {2003},
    EDITOR      = {},
    PAGES       = {},
    PUBLISHER   = {},
    VOLUME      = {},
    NUMBER      = {},
    SERIES      = {GI Lecture Notes in Informatics},
    ADDRESS     = {Frankfurt, Germany},
    MONTH       = {September},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://ai1.inf.uni-bayreuth.de/forschung/publikationsliste/2003/EMH_informatik2003.pdf},
    ABSTRACT    = {Clustering documents into classes is an important task in many Information
           Retrieval (IR) systems. This achieved grouping enables a description of the contents
           of the document collection in terms of the classes the documents fall into. The compactness
           of such a description is even more desirable in cases where the document
           collection is spread across different computers and locations; document classes can
           then be used to describe each partial document collection in a conveniently short form
           that can easily be exchanged with other nodes on the network. Unfortunately, most
           clustering schemes cannot easily be distributed. Additionally, the costs of transferring
           all data to a central clustering service are prohibitive in large-scale systems. In this
           paper, we introduce an approach which is capable of classifying documents that are
           distributed across a Peer-to-Peer (P2P) network. We present measurements taken on a
           P2P network using synthetic and real-world data sets.},
}