Expectation maximization algorithm cluster analysis for UK National Trust visitors

Shuang Cang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This article aims to investigate the segmenting of UK National Trust (NT) visitors based on behavior and motivation for the visit. The main focus of the article is to apply the more powerful, robust, and stable expectation maximization (EM) algorithm cluster analysis method together with PCA (without varimax rotation), which is rarely used in a tourism context, to the NT data set. This study identifies four clusters of NT visitors, and also identifies the most important items (questions) in the classification of NT visitors, which is the satisfaction with the NT service. The intracluster inequality, which means the diversity of the cluster, is also analyzed. Each cluster has its own characteristics and the results of cluster analysis will be useful for future NT marketing management to maximize the benefit to the NT. The diversity of each cluster is also discussed.

Original languageEnglish
Pages (from-to)637-650
Number of pages14
JournalTourism Analysis
Volume14
Issue number5
DOIs
Publication statusPublished - 1 Dec 2009

Keywords

  • Cluster analysis
  • Expectation maximization algorithm
  • K-means
  • Principal components analysis
  • UK National Trust

Fingerprint

Dive into the research topics of 'Expectation maximization algorithm cluster analysis for UK National Trust visitors'. Together they form a unique fingerprint.

Cite this