Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data

Amagata, Daichi

doi:https://doi.org/10.1109/BigData55660.2022.10020690

Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data

Amagata, Daichi

Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 2022, 445-454

Number of Access:489（2025-10-07 04:02 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92845

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Title	Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data
Creator	NRID 1000040770649 1000040770649 researchmap permalink amgt amgt Scopus Author ID 55811167300 55811167300 CreatorName Amagata, Daichi
Subject	density-peaks clustering
	dynamic data
	metric space
Description	Amagata D., . Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 , 445 (2022); https://doi.org/10.1109/BigData55660.2022.10020690.
Abstract	Clustering is a primitive and important operator that analyzes a given dataset to discover its hidden patterns and features. Because datasets are usually updated dynamically (i.e., it accepts continuous insertions and arbitrary deletions), analyzing such dynamic data is also an important topic, and dynamic clustering effectively supports it, but is a challenging problem. In this paper, we consider the problem of density-peaks clustering (DPC) on dynamic data. DPC is one of the density-based clustering algorithms and attracts attention for many applications, due to its effectiveness. We investigate the hardness of this problem theoretically to measure the efficiencies of dynamic DPC algorithms. We prove that any exact solutions are costly, and propose an approximation algorithm to enable faster updates. We conduct experiments on real datasets, and the results confirm that our algorithm is much faster and more accurate than state-of-the-art.
Conference Information	Conference Name 2022 IEEE International Conference on Big Data Conference Date December 17th to December 20th, 2022 Conference Venue International Convention Center Conference Place Osaka Conference Country Japan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Page	445-454
Date of Issued	2022-12-17
Language	English
Handle URL	https://hdl.handle.net/11094/92845
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1109/BigData55660.2022.10020690
Relation.isPartOf	ISBN 9781665480451
Relation.isPartOf	ISBN 9781665480468
Access Rights	open access
CopyRight	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCMI Type	Text
DCTERMS.bibliographicCitation	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. 2022, p. 445-454
DC.title	Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data
DC.creator	Amagata, Daichi
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2022-12-17
DC.identifier	https://doi.org/10.1109/BigData55660.2022.10020690
DC.subject	density-peaks clustering
	dynamic data
	metric space
DC.description	Amagata D., . Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 , 445 (2022); https://doi.org/10.1109/BigData55660.2022.10020690.
DCTERMS.abstract	Clustering is a primitive and important operator that analyzes a given dataset to discover its hidden patterns and features. Because datasets are usually updated dynamically (i.e., it accepts continuous insertions and arbitrary deletions), analyzing such dynamic data is also an important topic, and dynamic clustering effectively supports it, but is a challenging problem. In this paper, we consider the problem of density-peaks clustering (DPC) on dynamic data. DPC is one of the density-based clustering algorithms and attracts attention for many applications, due to its effectiveness. We investigate the hardness of this problem theoretically to measure the efficiencies of dynamic DPC algorithms. We prove that any exact solutions are costly, and propose an approximation algorithm to enable faster updates. We conduct experiments on real datasets, and the results confirm that our algorithm is much faster and more accurate than state-of-the-art.
DC.rights	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DC.format	92845
citation_title	Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data
citation_author	Amagata, Daichi
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2022-12-17
citation_journal_title	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
citation_firstpage	445
citation_lastpage	454
citation_doi	https://doi.org/10.1109/BigData55660.2022.10020690
citation_public_url	https://hdl.handle.net/11094/92845

Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data

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