An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

Mojahed, Aalaa and de la Iglesia, Beatriz (2017) An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach. Knowledge and Information Systems, 50 (1). pp. 27-52. ISSN 0219-1377

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    Abstract

    This paper introduces Hk-medoids, a modified version of the standard k-medoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk-medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm, and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k-medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation.

    Item Type: Article
    Uncontrolled Keywords: heterogeneous data,k-medoids,uncertainty,data fusion,clustering,smf
    Faculty \ School: Faculty of Science > School of Computing Sciences
    Depositing User: Pure Connector
    Date Deposited: 06 Apr 2016 09:30
    Last Modified: 11 Apr 2019 14:54
    URI: https://ueaeprints.uea.ac.uk/id/eprint/58143
    DOI: 10.1007/s10115-016-0930-3

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