Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Comparing performance of feed-forward neural nets and k-means for cluster-based market segmentation |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| Journal Title | European Journal of Operational Research |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0377-2217 |
| Volume | 114 |
| Number | 2 |
| Page Range | 346 - 353 |
| Date | 1999 |
| Abstract Text | We compare the performance of a specifically designed feedforward artificial neural network with one layer of hidden units to the K-means clustering technique in solving the problem of cluster-based market segmentation. The data set analyzed consists of usages of brands (product category: household cleaners) in different usage situations. The proposed feedforward neural network model results in a two segment solution that is confirmed by appropriate tests. On the other hand, the K-means algorithm fails in discovering any somewhat stronger cluster structure. Classification of respondents on the basis of external criteria is better for the neural network solution. We also demonstrate the managerial interpretability of the network results. |
| Digital Object Identifier | 10.1016/S0377-2217(98)00170-2 |
| Other Identification Number | merlin-id:14215 |
| Export |
BibTeX
EP3 XML (ZORA)
|