Mature market segmentation: a comparison of artificial neural networks and traditional methods

被引：9
|
作者：
Bigne, Enrique [1 ]
Aldas-Manzano, Joaquin [1 ,2 ]
Kuester, Ines [1 ]
Vila, Natalia [1 ]
机构：
[1] Univ Valencia, Fac Econ, Valencia 46022, Spain
[2] IVIE, Valencia 46022, Spain
来源：
NEURAL COMPUTING & APPLICATIONS | 2010年 / 19卷 / 01期
关键词：
Neural networks; Kohonen self-organising maps; Cluster analysis; Market segmentation; Mature market; K-MEANS; PERFORMANCE; BEHAVIOR; MODEL; OLD; MAP;
D O I：
10.1007/s00521-008-0226-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The need for in-depth knowledge of mature market segments and the need to overcome the limitations of using traditional methods to segment them motivate this study. The research objectives are (1) to examine neural networks, specifically Kohonen's self-organising maps (SOM), as an alternative to traditional statistical segmentation methods (hierarchical and non-hierarchical cluster analysis) and (2) to identify segments in the mature market which may direct its targeting. The results show the superiority of non-hierarchical clustering and SOM over hierarchical clustering, and demonstrate their complementary nature. In addition, significant segments with particular characteristics are found in the mature market.
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页码：1 / 11
页数：11
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