Summary
Well-known cluster analysis techniques like for instance the K-means method can be improved in almost every case by using adaptive distances. For this one has to estimate at least “appropriate” weights of variables, i.e. appropriate contributions to cluster analysis. Recently, adaptive classification techniques for two class models are under development. Here usually both the weights of variables and the weights (masses) of observations play an important role. For instance, observations that are harder to classify get increasingly larger weights. Quite successful applications of these techniques can be reported from the area of credit scoring systems for consumer loans or credit cards. The software ClusCorr (running under Microsoft EXCEL) perform classification, cluster analysis and multivariate graphics of (huge) high-dimensional data sets containing numerical values (quantitative or categorical) as well as non-numerical information.
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Mucha, HJ., Siegmund-Schultze, R., Dübon, K. (1998). Adaptive Cluster Analysis Techniques — Software and Applications. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_24
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DOI: https://doi.org/10.1007/978-4-431-65950-1_24
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