Articles
A COMPARISON OF SOM NEURAL NETWORKS AND K-MEANS CLUSTERING USING REAL WORLD DATA: CHINESE CONSUMER ATTITUDES TOWARDS IMPORTED FRUIT
Article number
566_21
Pages
185 – 191
Language
English
Abstract
SOM neural networks are regarded as a new clustering technique in market research.
However for the technique to be widely adopted in practice, it should demonstrate superiority over traditional clustering methods.
In this research we compared SOM neural networks with K-means algorithms to test their relative ability to generate reliable clustering solutions using real world data – Chinese consumer attitudes towards imported fruit.
Results show that K-means performs better than SOM in terms of reliability, but SOMs strength is its ability to discover the natural number of clusters.
However for the technique to be widely adopted in practice, it should demonstrate superiority over traditional clustering methods.
In this research we compared SOM neural networks with K-means algorithms to test their relative ability to generate reliable clustering solutions using real world data – Chinese consumer attitudes towards imported fruit.
Results show that K-means performs better than SOM in terms of reliability, but SOMs strength is its ability to discover the natural number of clusters.
Authors
X. Sun, R. Collins, J. Kim
Keywords
Market segmentation, discriminant analysis, reliability tests, consumer behavior, clustering
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