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Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering

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Abstract

Today, it is well known that taking into account the semantic information available for categorical variables sensibly improves the meaningfulness of the final results of any analysis. The paper presents a generalization of mixed Gibert’s metrics, which originally handled numerical and categorical variables, to include also semantic variables. Semantic variables are defined as categorical variables related to a reference ontology (ontologies are formal structures to model semantic relationships between the concepts of a certain domain). The superconcept-based distance (SCD) is introduced to compare semantic variables taking into account the information provided by the reference ontology. A benchmark shows the good performance of SCD with respect to other proposals, taken from the literature, to compare semantic features. Mixed Gibert’s metrics is generalized incorporating SCD. Finally, two real applications based on touristic data show the impact of the generalized Gibert’s metrics in clustering procedures and, in consequence, the impact of taking into account the reference ontology in clustering. The main conclusion is that the reference ontology, when available, can sensibly improve the meaningfulness of the final clusters.

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Acknowledgments

This work is partially supported by the Spanish Ministry of Science and Innovation (DAMASK, TIN2009-11005) in the Spanish Government PlanE (Spanish Economy and Employment Stimulation Plan). Montserrat Batet has been supported by a research grant provided by the Universitat Rovira i Virgili. The testing part has been possible thanks to the data provided by “Observatori de la Fundació d’Estudis Turístics Costa Daurada” and “Parc Nacional del Delta de l’Ebre (Departament de Medi Ambient i Habitatge, Generalitat de Catalunya).” Thanks to S. Clavé for his close collaboration. The authors also acknowledge the collaboration of E. Fourier, D. Corcho, N. Malé and N. Corral in the data preparation.

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  1. Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain

    Karina Gibert

  2. Dept. Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain

    Aïda Valls & Montserrat Batet

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  1. Karina Gibert
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Gibert, K., Valls, A. & Batet, M. Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering. Knowl Inf Syst 40, 559–593 (2014). https://doi.org/10.1007/s10115-013-0663-5

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