Data mining in a bicriteria clustering problem
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| Title: | Data mining in a bicriteria clustering problem |
|---|---|
| Authors: | Abascal, E.1, Garcia Lautre, I.1, Mallor, F. mallor@unavarra.es |
| Source: | European Journal of Operational Research. Sep2006, Vol. 173 Issue 3, p705-716. 12p. |
| Subjects: | Data mining, Factor analysis, Knowledge management, Executives |
| Abstract: | Abstract: 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. [Copyright &y& Elsevier] |
| Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 21188526 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data mining in a bicriteria clustering problem – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Abascal%2C+E%2E%22">Abascal, E.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Garcia+Lautre%2C+I%2E%22">Garcia Lautre, I.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mallor%2C+F%2E%22">Mallor, F.</searchLink><i> mallor@unavarra.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Sep2006, Vol. 173 Issue 3, p705-716. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+management%22">Knowledge management</searchLink><br /><searchLink fieldCode="DE" term="%22Executives%22">Executives</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: 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. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ejor.2005.10.006 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 705 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Factor analysis Type: general – SubjectFull: Knowledge management Type: general – SubjectFull: Executives Type: general Titles: – TitleFull: Data mining in a bicriteria clustering problem Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Abascal, E. – PersonEntity: Name: NameFull: Garcia Lautre, I. – PersonEntity: Name: NameFull: Mallor, F. IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 09 Text: Sep2006 Type: published Y: 2006 Identifiers: – Type: issn-print Value: 03772217 Numbering: – Type: volume Value: 173 – Type: issue Value: 3 Titles: – TitleFull: European Journal of Operational Research Type: main |
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