TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams.
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| Title: | TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams. |
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| Authors: | Bechini, Alessio1 (AUTHOR) a.bechini@ing.unipi.it, Marcelloni, Francesco1 (AUTHOR) francesco.marcelloni@unipi.it, Renda, Alessandro1 (AUTHOR) alessandro.renda@unifi.it |
| Source: | IEEE Transactions on Fuzzy Systems. Mar2022, Vol. 30 Issue 3, p623-637. 15p. |
| Subjects: | Fuzzy algorithms, Application software, Data structures, Electronic data processing, Parallel algorithms, Algorithms |
| Abstract: | In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e., a modification over time in the underlying data generation process. In this article, we propose temporal streaming fuzzy density-based spatial clustering of applications with noise (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage, continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real-world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Fuzzy Systems is the property of IEEE 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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| Items | – Name: Title Label: Title Group: Ti Data: TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bechini%2C+Alessio%22">Bechini, Alessio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> a.bechini@ing.unipi.it</i><br /><searchLink fieldCode="AR" term="%22Marcelloni%2C+Francesco%22">Marcelloni, Francesco</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> francesco.marcelloni@unipi.it</i><br /><searchLink fieldCode="AR" term="%22Renda%2C+Alessandro%22">Renda, Alessandro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alessandro.renda@unifi.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Fuzzy+Systems%22">IEEE Transactions on Fuzzy Systems</searchLink>. Mar2022, Vol. 30 Issue 3, p623-637. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fuzzy+algorithms%22">Fuzzy algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Application+software%22">Application software</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e., a modification over time in the underlying data generation process. In this article, we propose temporal streaming fuzzy density-based spatial clustering of applications with noise (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage, continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real-world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Fuzzy Systems is the property of IEEE 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: 10.1109/TFUZZ.2020.3042645 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 623 Subjects: – SubjectFull: Fuzzy algorithms Type: general – SubjectFull: Application software Type: general – SubjectFull: Data structures Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Parallel algorithms Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bechini, Alessio – PersonEntity: Name: NameFull: Marcelloni, Francesco – PersonEntity: Name: NameFull: Renda, Alessandro IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 10636706 Numbering: – Type: volume Value: 30 – Type: issue Value: 3 Titles: – TitleFull: IEEE Transactions on Fuzzy Systems Type: main |
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