2020
DOI: 10.3390/su12041536
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Challenge for Planning by Using Cluster Methodology: The Case Study of the Algarve Region

Abstract: This study analyses the most appropriate methodology to make similarity classifications among the cities of the Algarve (Portugal) according to 105 sustainability indicators monitored with the Observatory of Sustainability of the Algarve Region for Tourism (OBSERVE). The methodology used to establish the similarities was the cluster analysis with 4 different approaches which reduced the dimensions of the data set: total approach, pillar approach, subject area approach, and indicator approach. By combining the … Show more

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Cited by 4 publications
(3 citation statements)
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“…To analyse the similarity among the coastal cities, unidimensional cluster analyses were performed [87] . For this purpose, k-means was used; this is an iterative resignation algorithm based on the centroid concept of clusters of observations [88] .…”
Section: Methodsmentioning
confidence: 99%
“…To analyse the similarity among the coastal cities, unidimensional cluster analyses were performed [87] . For this purpose, k-means was used; this is an iterative resignation algorithm based on the centroid concept of clusters of observations [88] .…”
Section: Methodsmentioning
confidence: 99%
“…This value represents the ratio of the total between-groups sum of squares (BSS) to the total sum of squares (TSS). Higher values of BSS/TSS entail a better cluster separation [46]. This procedure was executed for each grid shift, extrapolating, among 88 values (one for every clustering algorithm (11) performed for each grid shift (4), first considering only urbanized and non-urbanized areas, then considering all the main categories), the best ones with the belonging method (Table 1).…”
Section: Unsupervised Machine Learning Proceduresmentioning
confidence: 99%
“…Traditionally, it is the most widely used supervised inductive learning classification technique used in the decision-making process and has the advantage that the connections between nodes can be expressed at the computational level as if-then rules, which facilitates their programming in different programming languages [69]. Due to this aspect, it is possible to establish the relationships between the input and output variables that best group the data set.…”
Section: Artificial Intelligence Analysismentioning
confidence: 99%