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Clustering an interval data set : are the main partitions similar to a priori partition?
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Orientador(es)
Resumo(s)
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.
Palavras-chave
Hierarchical Clustering Symbolic Data Interval Data Weighted Generalised Affinity Coefficient Probabilistic Aggregation Criteria VL Methodology Validations Measures
Contexto Educativo
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
Editora
International Journal of Current Research