A Fusion Approach to Computing Distance for Heterogeneous Data

Mojahed, Aalaa and De La Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 (2014) A Fusion Approach to Computing Distance for Heterogeneous Data. In: SCITEPRESS Digital Library - KDIR 2014 - International Conference on Knowledge Discovery and Information Retrieval, 2014-11-13, Italy.

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In this paper, we introduce heterogeneous data as data about objects that are described by different data types, for example, structured data, text, time series, images etc. We provide an initial definition of a heterogeneous object using some basic data types, namely structured and time series data, and make the definition extensible to allow for the introduction of further data types and complexity in our objects. There is currently a lack of methods to analyse and, in particular, to cluster such data. We then propose an intermediate fusion approach to calculate distance between objects in such datasets. Our approach deals with uncertainty in the distance calculation and provides a representation of it that can later be used to fine tune clustering algorithms. We provide some initial examples of our approach using a real dataset of prostate cancer patients including visualisation of both distances and uncertainty. Our approach is a preliminary step in the clustering of such heterogeneous objects as the distance between objects produced by the fusion approach can be fed to any standard clustering algorithm. Although further experimental evaluation will be required to fully validate the Fused Distance Matrix approach, this paper presents the concept through an example and shows its feasibility. The approach is extensible to other problems with objects represented by different data types, e.g. text or images.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
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: 25 Feb 2015 06:21
Last Modified: 19 Apr 2023 01:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/52360
DOI: 10.5220/0005083702690276

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