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

Author
Gibert, Karina; Valls, A.; Batet, M.
Type of activity
Journal article
Journal
Knowledge and information systems
Date of publication
2014-09-01
Volume
40
Number
3
First page
559
Last page
593
DOI
https://doi.org/10.1007/s10115-013-0663-5 Open in new window
Repository
http://hdl.handle.net/2117/28467 Open in new window
URL
http://link.springer.com/article/10.1007%2Fs10115-013-0663-5 Open in new window
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 ...
Citation
Gibert, Karina; Valls, A.; Batet, M. Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering. "Knowledge and information systems", 01 Setembre 2014, vol. 40, núm. 3, p. 559-593.
Keywords
BACKGROUND KNOWLEDGE, Clustering, DOMAIN, GENE ONTOLOGY, METRICS, Metrics, Numerical and Categorical variables, Ontology, PROFILES, RECOMMENDATIONS, SIMILARITY, SYSTEMS, Semantic data, TOURISM, WEB
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants

  • Gibert, Karina  (author)
  • Valls Mateu, Aïda  (author)
  • Batet Sanromà, Montserrat  (author)