Name
Title | ||
|---|---|---|
An extended self-organizing map network for market segmentation: a telecommunication example |
Abstract | ||
|---|---|---|
Kohonen's self-organizing map (SOM) network is an unsupervised learning neural network that maps an n-dimensional input data to a lower dimensional output map while maintaining the original topological relations. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. In this research effort, we applied this extended version of SOM networks to a consumer data set from American Telephone and Telegraph Company (AT&T). Results using the AT&T data indicate that the extended SOM network performs better than the two-step procedure that combines factor analysis and K-means cluster analysis in uncovering market segments. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.dss.2004.09.012 | Decision Support Systems |
Keywords | Field | DocType |
extended version,neural network,extended som network,factor analysis,n-dimensional input data,market segmentation,k-means cluster analysis,som neural network,telecommunication example,output map,self-organizing map,consumer data,som network,lower dimensional output map,extended self-organizing map network,cluster,cluster analysis,telecommunication,self organization,segmentation,k means algorithm,topology,network performance,market structure,unsupervised learning,market survey,k means clustering,classification | Data mining,k-means clustering,Market segmentation,Computer science,Segmentation,Self-organization,Self-organizing map,Unsupervised learning,Artificial intelligence,Artificial neural network,Market research | Journal |
Volume | Issue | ISSN |
42 | 1 | Decision Support Systems |
Citations | PageRank | References |
30 | 1.44 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Melody Y. Kiang | 1 | 259 | 25.18 |
| Michael Y. Hu | 2 | 426 | 55.74 |
| Dorothy M. Fisher | 3 | 57 | 5.49 |