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.
Original language | English |
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Pages (from-to) | 27-52 |
Number of pages | 26 |
Journal | Knowledge and Information Systems |
Volume | 50 |
Issue number | 1 |
Early online date | 18 Mar 2016 |
DOIs | |
Publication status | Published - Jan 2017 |
Keywords
- Heterogeneous data
- k-medoids
- Uncertainty
- Data fusion
- Clustering
- SMF
Profiles
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Beatriz De La Iglesia
- School of Computing Sciences - Professor & Head of School
- Norwich Institute for Healthy Aging - Member
- Norwich Epidemiology Centre - Member
- Data Science and Statistics - Member
Person: Research Group Member, Research Centre Member, Academic, Teaching & Research