Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.3/3771
Título: Clustering an interval data set : are the main partitions similar to a priori partition?
Autor: Sousa, Áurea
Bacelar-Nicolau, Helena
Nicolau, Fernando C.
Silva, Osvaldo
Palavras-chave: Hierarchical Clustering
Symbolic Data
Interval Data
Weighted Generalised Affinity Coefficient
Probabilistic Aggregation Criteria
VL Methodology
Validations Measures
Data: Nov-2015
Editora: International Journal of Current Research
Citação: Sousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, Osvaldo (2015). "Clustering an interval data set: are the main partitions similar to a priori partition?". International Journal of Current Research, Vol. 7, Nº 11, pp. 23151-23157. ISSN: 0975-833X
Resumo: In this paper we compare the best partitions of data units (cities) obtained from different algorithms of Ascendant Hierarchical Cluster Analysis (AHCA) of a well-known data set of the literature on symbolic data analysis (“city temperature interval data set”) with a priori partition of cities given by a panel of human observers. The AHCA was based on the weighted generalised affinity with equal weights, and on the probabilistic coefficient associated with the asymptotic standardized weighted generalized affinity coefficient by the method of Wald and Wolfowitz. These similarity coefficients between elements were combined with three aggregation criteria, one classical, Single Linkage (SL), and the other ones probabilistic, AV1 and AVB, the last ones in the scope of the VL methodology. The evaluation of the partitions in order to find the partitioning that best fits the underlying data was carried out using some validation measures based on the similarity matrices. In general, global satisfactory results have been obtained using our methods, being the best partitions quite close (or even coinciding) with the a priori partition provided by the panel of human observers.
Descrição: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Peer review: yes
URI: http://hdl.handle.net/10400.3/3771
ISSN: 0975-833X
Versão do Editor: http://www.journalcra.com/sites/default/files/11480_0.pdf
Aparece nas colecções:DME - Artigos em Revistas Internacionais / Articles in International Journals

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