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 ORCID: https://orcid.org/0000-0003-2675-5826 (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
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Pure Connector
Date Deposited:	06 Apr 2016 08:30
Last Modified:	19 Apr 2023 23:56
URI:	https://ueaeprints.uea.ac.uk/id/eprint/58143
DOI:	10.1007/s10115-016-0930-3

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