Comparative study of matrix refinement approaches for ensemble clustering.
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| Title: | Comparative study of matrix refinement approaches for ensemble clustering. |
|---|---|
| Authors: | Iam-On, Natthakan1 nt.iamon@gmail.com, Boongoen, Tossapon2 tossapon_b@rtaf.mi.th |
| Source: | Machine Learning. Jan2015, Vol. 98 Issue 1-2, p269-300. 32p. |
| Subjects: | Matrix groups, Matrix functions, Cluster analysis (Statistics), Multivariate analysis, Statistical ensembles |
| Abstract: | Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique. Since founded, different research areas have emerged with the common purpose of enhancing the effectiveness and applicability of cluster ensembles. These include the selection of ensemble members, the imputation of missing values, and the summarization of ensemble members. In particular, this paper is set to provide the review of different matrix refinement approaches that have been recently proposed in the literature for summarizing information of multiple clusterings. With various benchmark datasets and quality measures, the comparative study of these novel techniques is carried out to provide empirical findings from which a practical guideline can be drawn. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning is the property of Springer Nature 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: 100302977 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparative study of matrix refinement approaches for ensemble clustering. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Iam-On%2C+Natthakan%22">Iam-On, Natthakan</searchLink><relatesTo>1</relatesTo><i> nt.iamon@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Boongoen%2C+Tossapon%22">Boongoen, Tossapon</searchLink><relatesTo>2</relatesTo><i> tossapon_b@rtaf.mi.th</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. Jan2015, Vol. 98 Issue 1-2, p269-300. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Matrix+groups%22">Matrix groups</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+functions%22">Matrix functions</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+ensembles%22">Statistical ensembles</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique. Since founded, different research areas have emerged with the common purpose of enhancing the effectiveness and applicability of cluster ensembles. These include the selection of ensemble members, the imputation of missing values, and the summarization of ensemble members. In particular, this paper is set to provide the review of different matrix refinement approaches that have been recently proposed in the literature for summarizing information of multiple clusterings. With various benchmark datasets and quality measures, the comparative study of these novel techniques is carried out to provide empirical findings from which a practical guideline can be drawn. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Learning is the property of Springer Nature 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: 10.1007/s10994-013-5342-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 269 Subjects: – SubjectFull: Matrix groups Type: general – SubjectFull: Matrix functions Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Multivariate analysis Type: general – SubjectFull: Statistical ensembles Type: general Titles: – TitleFull: Comparative study of matrix refinement approaches for ensemble clustering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Iam-On, Natthakan – PersonEntity: Name: NameFull: Boongoen, Tossapon IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 98 – Type: issue Value: 1-2 Titles: – TitleFull: Machine Learning Type: main |
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