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as a separate classification because of their
exclusivity. Member-owned clubs are the most
common type found in the United States. Members
own stock or shares in the club. When the time
comes that the member leaves the club, the shares
are sold and/or transferred. The `owners' make up
the bulk of the membership and are the group with
the most rights/privileges. These types are generally
governed by a board of governors, which is
comprised of elected full stock-holding members. It
is this group that sets guidelines and policies. A
general manager, generally from outside of the
membership, is retained to manage the property
and work with the board on long-range goals.
Member-owned clubs are generally operated as
not-for-profit organisations. Privately owned clubs
are owned by companies, corporations or individuals.
These are operated on a for-profit basis and
managed by an individual appointed by the owner.
While they rely on some member input, this is less
so than in member-owned clubs. Some clubs
provide access privileges known as reciprocal
agreements to their members and their guests at
tourism destinations worldwide.
cluster analysis
CLAYTONW.BARROWS,CANADA
Cluster analysis is a widely used family of multivariate
techniques for grouping individuals, objects
or behaviours into similar clusters. In tourism
research, for example, cluster analysis is often used
to identify market segments in order to improve
the effectiveness of marketing efforts. These
segments may be based on a variety of variables
including demographic characteristics of the tourists
�such as age, income, gender and location)
and trip characteristics �such as trip length,
purpose, group size and benefits). Cluster analysis
has also been used to develop a typology for
classifying destinations into a schema such as
developed/undeveloped, accessible/inaccessible
and natural/manmade �see typology, tourist).
The flexibility of cluster analysis to accommodate a
wide range of applications makes it one of the most
useful tools for understanding the natural structures
among observations.
There are several approaches to cluster analysis
that can be classified into two general categories:
hierarchical and non-hierarchical. The former uses
agglomerative procedures whereby each observation
or object �the individual visitor or attraction)
starts by defining its own group, but on subsequent
steps the two closest clusters are combined into a
new aggregate cluster. Eventually, all observations/
objects are combined into one large cluster. Nonhierarchical
clustering procedures take the opposite
approach whereby the observations included in the
study are split into common groups. An important
difference in these two approaches is that
hierarchical clustering assumes an underlying
hierarchial structure among objects �that is, all
individuals or attractions assigned to a cluster are
maintained throughout the process of clustering),
whereas in non-hierarchical clustering objects have
free assignment, depending upon the number and
underlying structure of the observations/objects.
Interpretation and validation of the resulting
clusters are important steps in cluster analysis. The
interpretation stage involves developing a profile of
each cluster and identifying the variables that
distinguish one cluster from another. This information
enables the researcher to develop substantive
descriptions of each of the respective clusters.
Validation in cluster analysis describes the process
to assess the generalisability or stability of the
clustering solutions. The most simple and direct
approach to evaluating validity involves cluster
analysing of two or more separate samples �or subsamples)
and then comparing the results to insure
correspondence. Profiling clusters using several
independent variables such as demographic and
behavioural descriptors also provides a means for
validation and further interpretation/explanation
for the identified clusters.
See also: classification; discriminant analysis;
marketing; multidimensional scaling
Further reading
cluster analysis 85
Green, P.E. and Carroll, J.D. �1978) Mathematical
Tools for Applied Multivariate Analysis, New York:
Academic Press. �Discusses quantitative tools
used to evaluate consumer behaviour.)
Hartigan, J.A. �1975) Clustering Algorithms, New