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A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes

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Abstract

Real-world data collections are often heterogeneous (represented by a set of mixed attributes data types: numerical, categorical and fuzzy); since most available similarity measures can only be applied to one type of data, it becomes essential to construct an appropriate similarity measure for comparing such complex data. In this paper, a framework of new and unified similarity measures is proposed for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes. Examples are used to illustrate, compare and discuss the applications and efficiency of the proposed approach to heterogeneous data comparison and clustering.

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Acknowledgments

The authors are grateful to the anonymous reviewers for their comments that helped improving this manuscript. They would like to acknowledge Alexandru Ardelean’s contribution to the collection of the student-accommodations data set. They would also like to acknowledge gratefully Shaista Rashid and Anna Palczewska for their continuous help and good suggestions.

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Authors and Affiliations

  1. Department of Computing, University of Bradford, Bradford, BD7 1DP, UK

    Yasmina Bashon, Daniel Neagu & Mick J. Ridley

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  1. Yasmina Bashon
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  2. Daniel Neagu
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  3. Mick J. Ridley
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Corresponding author

Correspondence to Yasmina Bashon.

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Communicated by V. Loia.

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Bashon, Y., Neagu, D. & Ridley, M.J. A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes. Soft Comput 17, 1595–1615 (2013). https://doi.org/10.1007/s00500-012-0974-6

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