Dependency-based feature selection for clustering symbolic data.
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| Title: | Dependency-based feature selection for clustering symbolic data. |
|---|---|
| Authors: | Luis Talavera |
| Source: | Intelligent Data Analysis. 2000, Vol. 4 Issue 1, p19. 10p. |
| Subjects: | Selection theorems, Algorithms, Machine learning |
| Abstract: | Feature selection is a central problem in data analysis that have received a significant amount of attention from several disciplines, such as machine learning or pattern recognition. However, most of the research has been addressed towards supervised tasks, paying little attention to unsupervised learning. In this paper, we introduce an unsupervised feature selection method for symbolic clustering tasks. Our method is based upon the assumption that, in the absence of class labels, we can deem as irrelevant those features that exhibit low dependencies with the rest of features. Experiments with several data sets demonstrate that the proposed approach is able to detect completely irrelevant features and that, additionally, it removes other features without significantly hurting the performance of the clustering algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 4832209 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dependency-based feature selection for clustering symbolic data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luis+Talavera%22">Luis Talavera</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. 2000, Vol. 4 Issue 1, p19. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Selection+theorems%22">Selection theorems</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Feature selection is a central problem in data analysis that have received a significant amount of attention from several disciplines, such as machine learning or pattern recognition. However, most of the research has been addressed towards supervised tasks, paying little attention to unsupervised learning. In this paper, we introduce an unsupervised feature selection method for symbolic clustering tasks. Our method is based upon the assumption that, in the absence of class labels, we can deem as irrelevant those features that exhibit low dependencies with the rest of features. Experiments with several data sets demonstrate that the proposed approach is able to detect completely irrelevant features and that, additionally, it removes other features without significantly hurting the performance of the clustering algorithm. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=4832209 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/IDA-2000-4103 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 19 Subjects: – SubjectFull: Selection theorems Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Dependency-based feature selection for clustering symbolic data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luis Talavera IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2000 Type: published Y: 2000 Identifiers: – Type: issn-print Value: 1088467X Numbering: – Type: volume Value: 4 – Type: issue Value: 1 Titles: – TitleFull: Intelligent Data Analysis Type: main |
| ResultId | 1 |
