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

アクセス数:489件（2025-10-07 02:09 集計）

固定URL: https://hdl.handle.net/11094/92845

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タイトル	Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data
著者	NRID 1000040770649 1000040770649 researchmap permalink amgt amgt Scopus Author ID 55811167300 55811167300 著者名 Amagata, Daichi
キーワード等	density-peaks clustering
	dynamic data
	metric space
内容	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.
抄録	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.
会議情報	会議名 2022 IEEE International Conference on Big Data 開催期間 December 17th to December 20th, 2022 開催会場 International Convention Center 開催地 Osaka 開催国日本
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
ページ	445-454
刊行年月	2022-12-17
言語	英語
ハンドルURL	https://hdl.handle.net/11094/92845
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1109/BigData55660.2022.10020690
関連情報 (isPartOf)	ISBN 9781665480451
関連情報 (isPartOf)	ISBN 9781665480468
アクセス権	open access
権利情報	© 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.
出版タイプ	Accepted Manuscript
カテゴリ	会議発表論文 Conference Paper

資源タイプ	会議発表論文
ローカル資源タイプ	会議発表論文
DCMI資源タイプ	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	英語
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	英語
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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