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Bagged Clustering and its application to tourism market segmentation
- Source :
- Expert Systems with Applications. 40:4944-4956
- Publication Year :
- 2013
- Publisher :
- Elsevier BV, 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 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.
- Subjects :
- Fuzzy clustering
Single-linkage clustering
Sample (statistics)
Fuzzy C-medoids
computer.software_genre
Tourism market segmentation
qualitative data
fuzzy c-medoids
tourism market segmentation
dissimilarity measures for quantitative and qualitative data
bagged clustering
normalized weighted shannon entropy
dissimilarity measures for quantitative and
Artificial Intelligence
Bagged Clustering Fuzzy C-medoids Dissimilarity measures for quantitative and qualitative data Tourism market segmentation Normalized weighted Shannon entropy
Segmentation
Cluster analysis
Mathematics
business.industry
Dissimilarity measures for quantitative and
Dendrogram
Qualitative data
General Engineering
Pattern recognition
Medoid
Computer Science Applications
Hierarchical clustering
Bagged Clustering
Normalized weighted Shannon entropy
Artificial intelligence
Data mining
business
computer
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 40
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi.dedup.....13ed02532f33265be853117bad91b7ca
- Full Text :
- https://doi.org/10.1016/j.eswa.2013.03.005