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- W2241674976 abstract "In this work we consider a clustering problem, that is, grouping a large set ofelements described by a large set of non-negative variables into homogeneous categoriesthat are not predefined. The novelty is that besides the quantitative criterion to differentiatevariables, as usual, there is also a qualitative criterion, that takes into account whetheror not the variables take the value zero . This problem was presented and studied bymeans of a certain family of parametric transformations of the data set and also by meansof Multiple Factor Analysis in [1]. In our work, we consider these parametric transformationsof the data set and we use Self-Organizing Maps ([4]) as clustering technique. Wecompare the results with those obtained in [1]. This problem came up when the managersof a big telecommunications company wanted to know about the structure of their clients?portfolio identifying different consumption profiles. The proposed methods have beentested on artificial data sets with similar statistical properties to those found in the realone" @default.
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- W2241674976 date "2006-01-01" @default.
- W2241674976 modified "2023-09-23" @default.
- W2241674976 title "ANALYSING A BICRITERIA CLUSTERING PROBLEM USING SOM" @default.
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