Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample. The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (BxC) medoids and the membership degrees of each unit to the different clusters. The second step consists in running a hierarchical clustering algorithm on the (BxC) medoids. The best partition of the medoids is obtained investigating properly the dendrogram. Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step. The effectiveness of the suggested procedure has been shown analyzing a suggestive tourism segmentation problem. We analyze two sample of tourists, each one attending a different cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior.

Bagged Clustering and its application to tourism market segmentation / DE GIOVANNI, Livia; Pierpaolo, D'Urso; Marta, Disegna; Riccardo, Massari. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 40:12(2013), pp. 4944-4956. [http://dx.doi.org/10.1016/j.eswa.2013.03.005]

Bagged Clustering and its application to tourism market segmentation

DE GIOVANNI, LIVIA;
2013

Abstract

Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample. The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (BxC) medoids and the membership degrees of each unit to the different clusters. The second step consists in running a hierarchical clustering algorithm on the (BxC) medoids. The best partition of the medoids is obtained investigating properly the dendrogram. Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step. The effectiveness of the suggested procedure has been shown analyzing a suggestive tourism segmentation problem. We analyze two sample of tourists, each one attending a different cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior.
Bagged Clustering Fuzzy C-medoids Dissimilarity measures for quantitative and qualitative data Tourism market segmentation Normalized weighted Shannon entropy
Bagged Clustering and its application to tourism market segmentation / DE GIOVANNI, Livia; Pierpaolo, D'Urso; Marta, Disegna; Riccardo, Massari. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 40:12(2013), pp. 4944-4956. [http://dx.doi.org/10.1016/j.eswa.2013.03.005]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/67856
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