ePrints.FRI - University of Ljubljana, Faculty of Computer and Information Science

Subgroup discovery in data sets with multi-dimensional responses

Lan Umek and Blaz Zupan (2011) Subgroup discovery in data sets with multi-dimensional responses. Intelligent Data Analysis, 15 (4). pp. 533-549.

PDF - Accepted Version
Download (1168Kb)


    Most of the present subgroup discovery approaches aim at finding subsets of attribute-value data with unusual distribution of a single output variable. In general, real-life problems may be described with richer, multi-dimensional descriptions of the outcome. The discovery task in such domains is to find subsets of data instances with similar outcome description that are separable from the rest of the instances in the input space. We have developed a technique that directly addresses this problem and uses a combination of agglomerative clustering to find subgroup candidates in the space of output attributes, and predictive modeling to score and describe these candidates in the input attribute space. Experiments with the proposed method on a set of synthetic and on a real social survey data set demonstrate its ability to discover relevant and interesting subgroups from the data with multi-dimensional responses.

    Item Type: Article
    Keywords: subgroup discovery, multi-dimensional responses, clustering, machine learning, data mining
    Related URLs:
    URLURL Type
    http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(id=8548436)Alternative location
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Divisions: Faculty of Computer and Information Science > Bioinformatics Laboratory
    Item ID: 1484
    Date Deposited: 14 Aug 2011 09:48
    Last Modified: 02 Dec 2013 13:42
    URI: http://eprints.fri.uni-lj.si/id/eprint/1484

    Actions (login required)

    View Item