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Bagged fuzzy clustering for fuzzy data: An application to a tourism market
- Publication Year :
- 2014
-
Abstract
- Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers’ place preferences and implications for place marketing are offered.
- Subjects :
- Chinese consumers
Fuzzy data
Information Systems and Management
Fuzzy clustering
Bagged clustering
business.industry
Computer science
Stability (learning theory)
Fuzzy C-means
Likert-type scales
Sample (statistics)
computer.software_genre
Machine learning
Fuzzy logic
Management Information Systems
Artificial Intelligence
scienze statistiche
Data mining
Artificial intelligence
Cluster analysis
business
computer
Software
Tourism
Subjects
Details
- Language :
- English
- ISSN :
- 09507051
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....9355f0f06ae89274cd116016275db63b