Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data

Áurea Sousa (), Osvaldo Silva, Leonor Bacelar-Nicolau, João Cabral and Helena Bacelar-Nicolau
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Áurea Sousa: Faculty of Sciences and Technology, CEEAplA and OSEAN, Universidade dos Açores, 9500-321 Ponta Delgada, Portugal
Osvaldo Silva: Faculty of Sciences and Technology, CICSNOVA.UAc, Universidade dos Açores, 9500-321 Ponta Delgada, Portugal
Leonor Bacelar-Nicolau: Faculty of Medicine, Institute of Preventive Medicine and Public Health & ISAMB/FM-UL, Universidade de Lisboa, 1649-028 Lisboa, Portugal
João Cabral: Faculty of Sciences and Technology, Universidade dos Açores, 9500-321 Ponta Delgada, Portugal
Helena Bacelar-Nicolau: Faculty of Psychology, Institute of Environmental Health (ISAMB/FM-UL), Universidade de Lisboa, 1649-013 Lisboa, Portugal

Stats, 2023, vol. 6, issue 4, 1-13

Abstract: From the affinity coefficient between two discrete probability distributions proposed by Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and extended it to different types of data, including for the case of complex and heterogeneous data within the scope of symbolic data analysis (SDA). In this study, we refer to the most significant partitions obtained using the hierarchical cluster analysis (h.c.a.) of two well-known datasets that were taken from the literature on complex (symbolic) data analysis. h.c.a. is based on the weighted generalized affinity coefficient for the case of interval data and on probabilistic aggregation criteria from a VL parametric family. To calculate the values of this coefficient, two alternative algorithms were used and compared. Both algorithms were able to detect clusters of macrodata (aggregated data into groups of interest) that were consistent and consonant with those reported in the literature, but one performed better than the other in some specific cases. Moreover, both approaches allow for the treatment of large microdatabases (non-aggregated data) after their transformation into macrodata from the huge microdata.

Keywords: interval data; hierarchical cluster analysis; weighted generalized affinity coefficient; discrete probability distributions (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
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