Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering
| Autor: | Karina Gibert, Aida Valls, Montserrat Batet |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic, Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili. |
| Rok vydání: | 2013 |
| Předmět: |
Matemàtiques i estadística::Investigació operativa::Programació matemàtica [Àrees temàtiques de la UPC]
Computer science Operations research Ontology (information science) Semantic data model computer.software_genre Clustering RECOMMENDATIONS DOMAIN Semantic similarity Numerical and Categorical variables SYSTEMS Artificial Intelligence Semantic computing Optimització i investigació operativa Cluster analysis Categorical variable TOURISM Ontology GENE ONTOLOGY Ontology-based data integration PROFILES METRICS 90B Operations research and management science WEB Hierarchical clustering Human-Computer Interaction Hardware and Architecture 0219-1377 BACKGROUND KNOWLEDGE SIMILARITY Semantic data Data mining computer Software Information Systems |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname Knowledge And Information Systems UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Repositori Institucional de la Universitat Rovira i Virgili Consejo Superior de Investigaciones Científicas (CSIC) |
| ISSN: | 0219-3116 0219-1377 |
| DOI: | 10.1007/s10115-013-0663-5 |
| Popis: | 10.1007/s10115-013-0663-5 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. |
| Databáze: | OpenAIRE |
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