Statistical Analysis of Management Data pp 453-485 | Cite as
Cluster Analysis
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Abstract
The objective of cluster analysis is to group observations (e.g., individuals) in such a way that the groups formed are as homogeneous as possible within each group and as different as possible across groups.
Keywords
Cluster Analysis Cluster Solution Group Observation Centroid Method Group Centroid
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Bibliography
Basic Technical Readings
- Sugar, C. A., & James, G. M. (2003). Finding the number of clusters in a data set: An information-theoretic approach. Journal of the American Statistical Association, 98(463), 750–763.CrossRefGoogle Scholar
- Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.CrossRefGoogle Scholar
Application Readings
- Askegaard, S., & Madsen, T. K. (1998). The local and the global: Exploring traits of homogeneity and heterogeneity in European food cultures. International Business Review, 7(6), 549–568.CrossRefGoogle Scholar
- Calantone, R. J., & Di Benedetto, C. A. (2007). Clustering product launches by price and launch strategy. The Journal of Business and Industrial Marketing, 22(1), 4–19.CrossRefGoogle Scholar
- DeSarbo, W. S., & De Soete, G. (1984). On the use of hierarchical clustering for the analysis of nonsymmetric proximities. Journal of Consumer Research, 11(1), 601–610.CrossRefGoogle Scholar
- Hall, E. H., Jr., & St. John, C. H. (1994). A methodological note on diversity measurement. Strategic Management Journal, 15(2), 153–168.CrossRefGoogle Scholar
- Helsen, K., & Green, P. E. (1991). A computational study of replicated clustering with an application to market segmentation. Decision Sciences, 22, 1124–1141.CrossRefGoogle Scholar
- Helsen, K., Jedidi, K., & DeSarbo, W. S. (1993). A new approach to country segmentation utilizing multinational diffusion patterns. Journal of Marketing, 57(4), 60–71.CrossRefGoogle Scholar
- Hultink, E. J., Griffin, A., Robben, H. S. J., & Hart, S. (1998). In search of generic launch strategies for new products. International Journal of Research in Marketing, 15, 269–285.CrossRefGoogle Scholar
- Kale, S. H. (1995). Grouping euroconsumers: A culture-based clustering approach. Journal of International Marketing, 3(3), 35–48.Google Scholar
- Kumar, V., Ganesh, J., & Echambadi, R. (1998). Cross-national diffusion research: What do we know and how certain are we? Journal of Product Innovation Management, 15(3), 255–268.CrossRefGoogle Scholar
- Oliver, R. L., & Anderson, E. (1995). Behavior- and outcome-based sales control systems: Evidence and consequences of pure-form and hybrid governance. Journal of Personal Selling & Sales Management, 15(4), 1–15.Google Scholar
- Sethi, S. P. (1971). Comparative cluster analysis for world markets. Journal of Marketing Research, 8(3), 348–354.CrossRefGoogle Scholar
- Sexton, D. E., Jr. (1974). A cluster analytic approach to market response functions. Journal of Marketing Research, 11(1), 109–114.CrossRefGoogle Scholar
- Srivatsava, R. K., Leone, R. P., & Shocker, A. D. (1981). Market structure analysis: Hierarchical clustering of products based on substitution in use. Journal of Marketing, 45(3), 38–48.CrossRefGoogle Scholar
- Steenkamp, J.-B. E. M. (2001). The role of national culture in international marketing research. International Marketing Review, 18(1), 30–44.CrossRefGoogle Scholar
- Vandermerwe, S., & L’Huillier, M.-A. (1989). Euro-consumers in 1992. Business Horizons, 32(1), 34–40.CrossRefGoogle Scholar
- Völckner, F., & Sattler, H. (2007). Empirical generalizability of consumer evaluations of brand extensions. International Journal of Research in Marketing, 24, 149–162.CrossRefGoogle Scholar
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