Some Trends in the Classification of Variables

Nicolau, Fernando C.; Bacelar-Nicolau, Helena

doi:10.1007/978-4-431-65950-1_7

Cited by 15 publications

(17 citation statements)

References 2 publications

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“…This coefficient measures the extent of a monotonic relation between square root profiles of probabilities, and is also suitable for ordinal variables, as is the case of this set of items. This measure of comparison between elements was combined with two classical aggregation criteria, Single Linkage (SL) and Complete Linkage (CL) [34], and three probabilistic aggregation criteria, AVL (Aggregation Validity Link), AV1, and AVB (Aggregation Validity B-Link) [35,36], within the scope of the VL Methodology (V for validity, L for linkage) (Figures 3 and 4). According to p. 89):…”

Section: Resultsmentioning

confidence: 99%

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The Impacts of COVID-19 Crisis over the Tourism Expectations of the Azores Archipelago Residents

et al. 2020

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Throughout history, times of crisis have always been preceded by periods of change. In fact, the current moment of health, economic, and social crisis that we are experiencing seems to be no exception. Tourism seems to be one of the most affected activities by the COVID-19 pandemic crisis—due not only the cancelled flights but also to the fear of being infected with the virus. In this sense, the present article intends to identify the Azores Archipelago residents’ tourism expectations during the COVID-19 crisis. Using the Azores Islands as a case study and merging it with the authors’ knowledge about this regional reality, it was possible to collect a substantial quantity of data. Thus, this study provides an in-depth perception of the main issues about how this pandemic crisis affects the tourism expectations of the Azores region residents, and consequently, how this situation will influence regional sustainable development. Through the study, it was possible to verify that the significant part of the Azores Archipelago residents (57.5%) will not be going to travel to spend vacations in 2020. Additionally, 84.2% will have holidays in the region; therefore, they stay in the Azores Archipelago. Besides, this study shows that majority of the respondents (61.6%) would not make a reservation for a 2020 vacation.

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Section: Resultsmentioning

confidence: 99%

“…According to the global statistics of levels, STAT [35,37], the best partition is a partition into three clusters obtained at level 3 by all aggregation criteria (STAT = 2.8723): Cluster 1: {i, ii}; Cluster 2: {iii, iv, v}; and Cluster 3: {vi}.…”

Section: Resultsmentioning

confidence: 99%

The Impacts of COVID-19 Crisis over the Tourism Expectations of the Azores Archipelago Residents

et al. 2020

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“…This coefficient was combined with 26 aggregation criteria, 12 of which were classical (SL, CL,…, AMGT) and 14 probabilistic (AVM, AVmg,…, AVMLD) (Nicolau, 1983;Nicolau and Bacelar-Nicolau, 1998). The indicators E(h), SIL(h) and Global_Ind(h) presented in Table 1 give detailed information on each of the data units h, taking into consideration the cluster to which they belong, how many data units are contained in the cluster with which the data unit h is associated, and its degree of homogeneity and isolation.…”

Section: Resultsmentioning

confidence: 99%

A Global Approach to the Comparison of Clustering Results

Silva

Bacelar-Nicolau

Nicolau

2012

Biometrical Letters

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SUMMARYThe discovery of knowledge in the case of Hierarchical Cluster Analysis (HCA) depends on many factors, such as the clustering algorithms applied and the strategies developed in the initial stage of Cluster Analysis. We present a global approach for evaluating the quality of clustering results and making a comparison among different clustering algorithms using the relevant information available (e.g. the stability, isolation and homogeneity of the clusters). In addition, we present a visual method to facilitate evaluation of the quality of the partitions, allowing identification of the similarities and differences between partitions, as well as the behaviour of the elements in the partitions. We illustrate our approach using a complex and heterogeneous dataset (real horse data) taken from the literature. We apply HCA based on the generalized affinity coefficient (similarity coefficient) to the case of complex data (symbolic data), combined with 26 (classic and probabilistic) clustering algorithms. Finally, we discuss the obtained results and the contribution of this approach to gaining better knowledge of the structure of data.

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“…Each interval variable (generalized column) gave a sub-table with a suitable number of columns corresponding to a set of elementary intervals. The affinity coefficient was combined with three probabilistic aggregation criteria, AVL, AV1, and AVB (Bacelar -Nicolau, 1988;Nicolau and Bacelar-Nicolau, 1998). Table 3 represents the similarity matrix, and Figures 1 and 2 show the dendrograms associated with the AVL and AV1/AVB aggregation criteria respectively.…”

Section: Case Study: Freshwater Fish Data Set (Ecotoxicology Data Set)mentioning

confidence: 99%

“…The probabilistic aggregation criteria used (AVL, AV1 and AVB) under the probabilistic approach of AHCA, named VL methodology (V for Validity, L for Linkage), resort essentially to probabilistic notions for the definition of the comparative functions. In fact, the VL-family is a set of agglomerative hierarchical clustering methods, based on the cumulative distribution function of basic similarity coefficients (Bacelar-Nicolau, 1980, 1988Nicolau and Bacelar-Nicolau, 1998). …”

Section: Introductionmentioning

confidence: 99%

Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set

Sousa

Bacelar-Nicolau

Nicolau

2013

Biometrical Letters

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SUMMARYIn this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors.

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Some Trends in the Classification of Variables

Cited by 15 publications

References 2 publications

The Impacts of COVID-19 Crisis over the Tourism Expectations of the Azores Archipelago Residents

The Impacts of COVID-19 Crisis over the Tourism Expectations of the Azores Archipelago Residents

A Global Approach to the Comparison of Clustering Results

Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set

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