On clustering interval data with different scales of measures : experimental results

This article is is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Attribution-NonCommercial (CC BY-NC) license lets others remix, tweak, and build upon work non-commercially, and although the new works must also acknowledge & be non-commercial.Symbolic Data Analysis can be defined as the extension of standard data analysis to more complex data tables. We illustrate the application of the Ascendant Hierarchical Cluster Analysis (AHCA) to a symbolic data set (with a known structure) in the field of the automobile industry (car data set), in which objects are described by variables whose values are intervals of the real data set (interval variables). The AHCA of thirty-three car models, described by eight interval variables (with different scales of measure), was based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. We applied three probabilistic aggregation criteria in the scope of the VL methodology (V for Validity, L for Linkage). Moreover, we compare the achieved results with those obtained by other authors, and with a priori partition into four clusters defined by the category (Utilitarian, Berlina, Sporting and Luxury) to which the car belong. We used the global statistics of levels (STAT) to evaluate the obtained partitions

Clustering an interval data set : are the main partitions similar to a priori partition?

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.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

Classificação e Análise de Dados – Métodos e Aplicações III

Os artigos incluídos neste livro, após processo de revisão por pares, são desenvolvimentos de trabalhos apresentados nas JOCLAD 2014-17 e mostram, mais uma vez, a interdisciplinariedade e a diversidade das áreas que integram esta Jornadas