This document discusses cluster analysis and its application in hospitality and tourism research. It provides examples of two studies that used cluster analysis: one identified five backpacker groups to Scotland based on characteristics, and the other classified consumers into two groups based on emotional criteria to show different characteristics. The document also outlines the cluster analysis process, which involves selecting variables, designing the research, checking assumptions, selecting a clustering algorithm, interpreting clusters, and validating results.

1. CLUSTER ANALYSIS
HTM 602
September 10, 2008
Suh-hee Choi

2. “Cluster analysis classifies
objects, so that each
object is similar to others
in the cluster with respect
to a predetermined
selection criterion.”
2
A
B
C
E
D
F
Hair, Joseph F. & William Black, Cluster analysis, in Grimm, Laurence & Paul Yarnold (eds.) , 2000, Reading and
Understanding More Multivariate Statistics, Ch. 5, P.147, American Psychological Association

3. CLUSTER ANALYSIS IN HOSPITALITY AND
TOURISM RESEARCH
 Thyne et al. (2004)* used cluster analysis to identify
backpacker groups to Scotland. They derived five
groups: Typical Backpackers, Discoverers,
Outdoors, Family Ties, and Routine Travelers.
 Bigné et al. (2004)** used this methodology to class
ify consumer into two groups to show that it is possi
ble to use emotional criteria to identify the character
istics of consumers.
3
* Thyne, M., Davies, S. & Rob Nash (2004) A Lifestyle Segmentation Analysis of the Backpacker Market in
Scotland: A Case Study of the Scottish Youth Hostel Association. Journal of Quality Assurance in Hospitality &
Tourism 5(2/3/4): 95 - 119
** Bigné, J. E. & Luisa Andreu (2004) Emotions in Segmentation: An Empirical Study. Annals of Tourism Resear
ch 31(3):682-696

4. CLUSTER ANALYSIS PROCESS
1. Research Problem
(Select Objectives / Select Clustering Variables)
2. Research Design
(Outliers / Standardization)
3. Assumptions (Is the sample representative of the population?
Is multicollinearity substantial enough to affect results?)
4. Selecting a Clustering Algorithm
(Hierarchical / Nonhierarchical)
Number of Clusters Formed
Cluster Analysis Respecification
5. Interpreting the Clusters
(Name clusters based on clustering variables)
6. Validating and Profiling the Clusters
4
Hair, Joseph F. & William Black, Cluster analysis, in Grimm, Laurence & Paul Yarnold (eds.) , 2000, Reading and
Understanding More Multivariate Statistics, Ch. 5, American Psychological Association

5.  Objectives
 Classifying customer based on emoti
onal criteria
 Variable selection (p.689)
 X1 (angry-satisfied), X2 (unhappy-happy),
… X10
5
1. Research Problem

6.  Can outliers be detected?
 How should object similarity be measured? (1)
6
2. Research Design
A
B
C

7. 7
2. Research Design
 How should object similarity be measured?
 Euclidean distance
Distance (O1, O2) =
 City-block approach
Distance (O1, O2) =
 Should the data be standardized?
Object

Probability of


i
1
i
Purchasing a brand
(  
)
1 2 2
i i
  
i i
1 2
Commercial Viewing time
Minutes Seconds
1
A 60 3 180
B 30 4 240

8.  Representativeness of the sample
 Multicollinearity
8
3. Assumptions

9. 9
4. Derivation of Clusters
object var1 var2 var3
A 4 6 6
B 3 3 3
C 3 4 3
D 4 6 7
E 7 6 6
Squared Euclidean Distance
A B C D E
A
B 1+32+32=19
C 1+22+32=14 0+1+0=1
D 0+0+1=1 1+32+42=26 1+22+42=21
E 32+0+0=9 42+32+32=34 42+22+32=29 32+0+1=10

10. 10
4. Derivation of Clusters
Squared Euclidean Distance
A B C D E
A
B 1+32+32=19
C 1+22+32=14 0+1+0=1
D 0+0+1=1 1+32+42=26 1+22+42=21
E 32+0+0=9 42+32+32=34 42+22+32=29 32+0+1=10
e.g. Agglomerative hierarchical clustering process
Step Minimum
distance
Pair Cluster No. of
clusters
1 1 B-C
A-D
(A,D), (B,C), E 3
2 9 A-E (A,D,E),(B,C) 2
3 10 D-E (A,B,C,D,E) 1

11. 11
4. Derivation of Clusters
Step Minimum
* Dandrogram
B
C
A
D
E
distance
Pair Cluster No. of
1 Distance at combination 9
clusters
1 1 B-C
A-D
(A,D), (B,C), E 3
2 9 A-E (A,D,E),(B,C) 2
3 10 D-E (A,B,C,D,E) 1
10
5. Interpretation
6. Validation