Show Reference: "Collaborative Data Mining"

Collaborative Data Mining In Data Mining and Knowledge Discovery Handbook (2005), pp. 1043-1056, doi:10.1007/0-387-25465-x_49 by Steve Moyle edited by Oded Maimon, Lior Rokach
@incollection{moyle-2005,
    abstract = {Collaborative Data Mining is a setting where the Data Mining effort is distributed to multiple collaborating agents — human or software. The objective of the collaborative Data Mining effort is to produce solutions to the tackled Data Mining problem which are considered better by some metric, with respect to those solutions that would have been achieved by individual, non-collaborating agents. The solutions require evaluation, comparison, and approaches for combination. Collaboration requires communication, and implies some form of community. The human form of collaboration is a social task. Organizing communities in an effective manner is non trivial and often requires well defined roles and processes. Data Mining, too, benefits from a standard process. This chapter explores the standard Data Mining process {CRISP}-{DM} utilized in a collaborative setting.},
    author = {Moyle, Steve},
    booktitle = {Data Mining and Knowledge Discovery Handbook},
    citeulike-article-id = {13578697},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/0-387-25465-x\_49},
    citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/0-387-25465-X\_49},
    doi = {10.1007/0-387-25465-x\_49},
    editor = {Maimon, Oded and Rokach, Lior},
    keywords = {data},
    pages = {1043--1056},
    posted-at = {2015-04-10 13:21:59},
    priority = {2},
    publisher = {Springer US},
    title = {Collaborative Data Mining},
    url = {http://dx.doi.org/10.1007/0-387-25465-x\_49},
    year = {2005}
}

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