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Data mining in a bicriteria clustering problem [An article from: European Journal of Operational Research]
Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
In this paper, we address the issue of clustering elements, described by a large set of non-negative variables, first using quantitative criteria to differentiate variable values, and then qualitative criteria to focus on whether or not the variables take a zero value. A zero value is relevant in a managerial context, for example, where it may indicate non-consumption of a certain product. In this case, a zero versus a positive value constitutes, in itself, an primary point of interest. This is the type of situation, moreover, in which there is usually a high frequency of zero values. We suggest two different approaches to the analysis of these data. One uses multiple factor analysis (MFA), which allows a compromise between qualitative and quantitative criteria. The other proposes a family of functions for transforming the original data in such a way that the parameter used to index the functions is interpreted as the weight assigned to each criterion. We have tested both procedures on a real-world data set to obtain a customer typology for a telecommunications company. The results were encouraging and useful to the managers.
Description:
In this paper, we address the issue of clustering elements, described by a large set of non-negative variables, first using quantitative criteria to differentiate variable values, and then qualitative criteria to focus on whether or not the variables take a zero value. A zero value is relevant in a managerial context, for example, where it may indicate non-consumption of a certain product. In this case, a zero versus a positive value constitutes, in itself, an primary point of interest. This is the type of situation, moreover, in which there is usually a high frequency of zero values. We suggest two different approaches to the analysis of these data. One uses multiple factor analysis (MFA), which allows a compromise between qualitative and quantitative criteria. The other proposes a family of functions for transforming the original data in such a way that the parameter used to index the functions is interpreted as the weight assigned to each criterion. We have tested both procedures on a real-world data set to obtain a customer typology for a telecommunications company. The results were encouraging and useful to the managers.
