Bagged Clustering and its application to tourism market segmentation
Authors: D'Urso, P., De Giovanni, L., Disegna, M. and Massari, R.
Journal: Expert Systems with Applications
Volume: 40
Issue: 12
Pages: 4944-4956
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2013.03.005
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 sam- ple, obtaining (B × C) medoids and the membership degrees of each unit to the different clusters.The sec- ond step consists in running a hierarchical clustering algorithm on the (B × C) 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 sug- gested procedure has been shown analyzing a suggestive tourism segmentation problem. Weanalyze two sample of tourists, each one attending adifferent cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior. © 2013 Elsevier Ltd. All rights reserved.
https://eprints.bournemouth.ac.uk/23312/
Source: Scopus
Bagged Clustering and its application to tourism market segmentation
Authors: D'Urso, P., De Giovanni, L., Disegna, M. and Massari, R.
Journal: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 40
Issue: 12
Pages: 4944-4956
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2013.03.005
https://eprints.bournemouth.ac.uk/23312/
Source: Web of Science (Lite)
Bagged Clustering and its application to tourism market segmentation
Authors: D'Urso, P., De Giovanni, L., Disegna, M. and Massari, R.
Journal: Expert Systems with Applications
Volume: 40
Issue: 12
Pages: 4944-4956
DOI: 10.1016/j.eswa.2013.03.005
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 sam- ple, obtaining (B × C) medoids and the membership degrees of each unit to the different clusters.The sec- ond step consists in running a hierarchical clustering algorithm on the (B × C) 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 sug- gested procedure has been shown analyzing a suggestive tourism segmentation problem. Weanalyze two sample of tourists, each one attending adifferent cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior. © 2013 Elsevier Ltd. All rights reserved.
https://eprints.bournemouth.ac.uk/23312/
Source: Manual
Bagged Clustering and its application to tourism market segmentation
Authors: D'Urso, P., De Giovanni, L., Disegna, M. and Massari, R.
Journal: Expert Systems with Applications
Volume: 40
Issue: 12
Pages: 4944-4956
ISSN: 0957-4174
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 sam- ple, obtaining (B × C) medoids and the membership degrees of each unit to the different clusters.The sec- ond step consists in running a hierarchical clustering algorithm on the (B × C) 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 sug- gested procedure has been shown analyzing a suggestive tourism segmentation problem. Weanalyze two sample of tourists, each one attending adifferent cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior. © 2013 Elsevier Ltd. All rights reserved.
https://eprints.bournemouth.ac.uk/23312/
Source: BURO EPrints