Fachbereich: Betriebswirtschaftslehre
<< zurück zur Ergebnisliste
vollständigen Artikel abrufen
The Number of Clusters in Market Segmentation
Learning the 'true' number of clusters in a given data set is a fundamental and largely unsolved problem in data analysis, which seriously affects the
identification of customer segments in marketing research.
In this paper, we discuss the properties of relevant criteria commonly used to
estimate the number of clusters. Moreover, we outline two adaptive clustering algorithms, a growing /c-means algorithm and a growing self-organizing neural network.
In the empirical part of the paper, we find that the first algorithm stops growing
with exactly the number of clusters that we get when determining the optimal
number of clusters by means of the Jt/MP-criterion. This cluster solution proves
to be rather similar to the one we obtain by applying the neural network approach.
To evaluate the clusters, we use association rules. By testing these rules, we show
the differences of patterns underlying particular market segments.
Autor
Prof. Dr. Ralf Wagner
Sören W. Scholz; Reinhold Decker
 
ArtikelFachbereichFachrichtung
2005BetriebswirtschaftslehreMarketing/Absatz
 
Schlagwörter
Association Rule, Market Segmentation, Multivariate Normal Distribution, Cluster Solution, Marketing Research