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Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering
Gibert Oliveras, Karina; Valls Mateu, Aïda; Batet Sanromà, Montserrat
Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic
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.
Peer Reviewed
Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica
Operations research
Clustering
Metrics
Numerical and Categorical variables
Semantic data
Ontology
BACKGROUND KNOWLEDGE
GENE ONTOLOGY
SIMILARITY
WEB
RECOMMENDATIONS
PROFILES
TOURISM
SYSTEMS
METRICS
DOMAIN
Optimització i investigació operativa
90B Operations research and management science
info:eu-repo/semantics/publishedVersion
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