Publication:

Segmentation Based Competitive Analysis with MULTICLUS and Topology Representing Networks

Date

Date

Date
2000
Journal Article
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Reutterer, T., & Natter, M. (2000). Segmentation Based Competitive Analysis with MULTICLUS and Topology Representing Networks. Computers & Operations Research, 27(11–12), 1227–1247. https://doi.org/10.1016/S0305-0548(99)00147-1

Abstract

Abstract

Abstract

Two neural network approaches, Kohonen's Self-Organizing (Feature) Map (SOM) and the Topology Representing Network (TRN) of Martinetz and Schulten are employed in the context of competitive market structuring and segmentation analysis. In an empirical study using brands preferences derived from household panel data, we compare the SOM and TRN approach to MULTICLUS, a parametric approach which also simultaneously solves the market structuring and segmentation problem. Our empirical analysis shows several benefits and shortcomings of th

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114 since deposited on 2017-11-21
Acq. date: 2025-11-14

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Journal/Series Title

Journal/Series Title

Journal/Series Title

Volume

Volume

Volume
27

Number

Number

Number
11-12

Page range/Item number

Page range/Item number

Page range/Item number
1227

Page end

Page end

Page end
1247

Item Type

Item Type

Item Type
Journal Article

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Scope

Scope

Scope
Discipline-based scholarship (basic research)

Language

Language

Language
English

Publication date

Publication date

Publication date
2000

Date available

Date available

Date available
2017-11-21

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Publisher

ISSN or e-ISSN

ISSN or e-ISSN

ISSN or e-ISSN
0305-0548

OA Status

OA Status

OA Status
Closed

Other Identification Number

Other Identification Number

Other Identification Number
merlin-id:14213

Metrics

Views

114 since deposited on 2017-11-21
Acq. date: 2025-11-14

Citations

Citation copied

Reutterer, T., & Natter, M. (2000). Segmentation Based Competitive Analysis with MULTICLUS and Topology Representing Networks. Computers & Operations Research, 27(11–12), 1227–1247. https://doi.org/10.1016/S0305-0548(99)00147-1

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