Bagged fuzzy clustering for fuzzy data: An application to a tourism market

D’Urso, Pierpaolo; Disegna, Marta; Massari, Riccardo; Prayag, Girish

doi:10.1016/j.knosys.2014.10.015

Cited by 44 publications

(21 citation statements)

References 99 publications

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“…This approach to segmentation casts doubt on the stability and reproducibility of the identified clusters. With the exception of a few studies (i.e., Molina et al, 2015) that use latent class analysis, robust segmentation methods such as SOM (Bloom, 2004(Bloom, , 2005Li, Law, & Wang, 2010;Mazanec, 1994), and ensemble methods among which BC (Dolnicar & Leisch, 2003;D'Urso, Disegna, Massari, & Prayag, 2015;Prayag, Disegna, Cohen, & Yan, 2015) are sparsely used in tourism studies. In particular, ensemble methods refer to a set of individually trained classifiers (such as neural networks and clustering methods) whose findings are combined to generate clusters (Opitz & Maclin, 1999).…”

Section: Methodsmentioning

confidence: 99%

Why wine tourists visit cellar doors: Segmenting motivation and destination image

Bruwer

Prayag

Disegna

2018

Journal of Tourism Research

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This study examines the relationship between the motivation of wine tourists to visit cellar doors and destination image perception. A survey of tourists resulted in 676 useable questionnaires. Using a novel segmentation method, self‐organizing maps, and bagged clustering, the study identified 5 distinct motivation clusters. These clusters were different on only gender and previous visit to the wine region. Three clusters of destination image were identified using the same segmentation method. Significant relationships were found between the motivation and destination image clusters. Implications for destination marketing and managing the tourist experience at the winery cellar door are discussed.

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Section: Methodsmentioning

confidence: 99%

Why wine tourists visit cellar doors: Segmenting motivation and destination image

Bruwer

Prayag

Disegna

2018

Journal of Tourism Research

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“…In general, internal validity measures provide useful guidelines in the identification of the best final partition (Handl et al, 2005;D'Urso et al, 2015). Xie & Beni (1991) and Campello & Hruschka (2006).…”

Section: Fuzzy Partition Validitymentioning

confidence: 99%

Analysing cluster evolution using repeated cross-sectional ordinal data

2018

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This study contributes to the existing literature on tourism market segmentation by providing a new matching-clustering procedure that allows patterns of behaviours to be identified using repeated cross-sectional surveys. By extracting equivalent samples over time, the matching method allows inter-temporal cluster analyses to be performed so that a deeper insight into a phenomenon can be obtained beyond the traditional aggregate level of understanding. The paper provides a step-by-step description of the matching-clustering procedure that can be easily replicated, both within and outside the tourism field, when repeated cross-sectional data are available. From a practical and managerial perspective, the proposed procedure helps destination managers and municipalities to describe and verify the efficacy of policy and strategies adopted over years without the necessity to rely on longitudinal surveys, which are often difficult to conduct.

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“…D'Urso and Giordani [26] suggested a fuzzy clustering method for symmetrical fuzzy data based on a weighted (squared) distance for fuzzy data. Suggestive applications of fuzzy clustering methods for fuzzy data have been suggested by D'Urso et al [23,25,24] and Disegna et al [14]. In a clusterwise framework, Yang and Ko [55], D'Urso and Santoro [30] and D'Urso et al [29] adopt a fuzzy clustering-based approach to overcome the heterogeneity problem in fuzzy regression analysis of fuzzy data.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering of fuzzy data based on robust loss functions and ordered weighted averaging

D’Urso

Łęski

2020

Fuzzy Sets and Systems

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In many real cases the data are not expressed in term of single values but are imprecise. In all these cases, standard clustering methods for single-valued data are unable to properly take into account the imprecise nature of the data. In this paper, by considering the Partitioning Around Medoids (PAM) approach in a fuzzy framework, we propose a fuzzy clustering method for imprecise data formalized in a fuzzy manner. In particular, in order to neutralize the negative effects of possible outlier fuzzy data in the clustering process, we proposed a robust fuzzy c-medoids clustering method for fuzzy data based on the combination of Huber's M-estimators and Yager's OWA (Ordered Weighted Averaging) operators. The proposed method is able to smooth the influence of anomalous data by means of a suitable parameter, the so-called typicality parameter, capable to tune the influence of the outliers. The performance of the proposed method has been shown by means of a simulation study, composed of experiments on: (i) simple two-dimensional dataset, (ii) benchmark datasets and (iii) the fuzzy-art-outliers dataset. The comparison made with the robust clustering methods known from the literature indicates the competitiveness of the introduced method to others. An application of the suggested method to a real dataset is also provided and the results of the method has been compared with other clustering methods suggested in the literature. In the application, the comparative assessment has shown the informational gain (in term of additional information) of the proposed method vs the other robust methods.

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Bagged fuzzy clustering for fuzzy data: An application to a tourism market

Cited by 44 publications

References 99 publications

Why wine tourists visit cellar doors: Segmenting motivation and destination image

Why wine tourists visit cellar doors: Segmenting motivation and destination image

Analysing cluster evolution using repeated cross-sectional ordinal data

Fuzzy clustering of fuzzy data based on robust loss functions and ordered weighted averaging

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