A buffer-based online clustering for evolving data stream

Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hyb...

Full description

Bibliographic Details
Main Authors:	Islam, Md. Kamrul, Ahmed, Md. Manjur, Kamal Z., Zamli
Format:	Article
Language:	English
Published:	Elsevier Ltd 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/24676/ http://umpir.ump.edu.my/id/eprint/24676/ http://umpir.ump.edu.my/id/eprint/24676/ http://umpir.ump.edu.my/id/eprint/24676/1/A%20buffer-based%20online%20clustering%20for%20evolving%20data%20stream.pdf

id	ump-24676
recordtype	eprints
spelling	ump-246762019-04-02T07:34:52Z http://umpir.ump.edu.my/id/eprint/24676/ A buffer-based online clustering for evolving data stream Islam, Md. Kamrul Ahmed, Md. Manjur Kamal Z., Zamli QA75 Electronic computers. Computer science Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hybrid online/offline, or cannot handle the property of evolving data stream. Recently, a fully online clustering algorithm for evolving data stream called CEDAS was proposed. However, similar to other density-based clustering algorithms, CEDAS requires predefining the global optimal radius of micro-clusters, which is a difficult task; in addition, an erroneous choice deteriorates cluster performance. Moreover, the algorithm ignores the presence of temporarily irrelevant micro-clusters, which may be relevant in the future. In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). This algorithm recursively updates the micro-cluster radius to its local optimal. It also introduces a buffer for storing irrelevant micro-clusters and a fully online pruning method for extracting the temporarily irrelevant micro-cluster from the buffer. In addition, BOCEDS proposes an online micro-cluster energy-updating function based on the spatial information of the data stream. Experimental results are compared with those of CEDAS and other alternative hybrid online/offline density-based clustering algorithms, and BOCEDS proves its superiority over the other clustering algorithms. The sensitivity of clustering parameters is also measured. The proposed algorithm is then applied to real-world weather data streams to demonstrate its capability to detect changes in data stream and discover arbitrarily shaped clusters. The proposed BOCEDS can be available in https://sites.google.com/view/md-manjur-ahmed and https://sites.google.com/view/kamrul-just. Elsevier Ltd 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24676/1/A%20buffer-based%20online%20clustering%20for%20evolving%20data%20stream.pdf Islam, Md. Kamrul and Ahmed, Md. Manjur and Kamal Z., Zamli (2019) A buffer-based online clustering for evolving data stream. Information Sciences, 489. pp. 113-135. ISSN 0020-0255 https://doi.org/10.1016/j.ins.2019.03.022 https://doi.org/10.1016/j.ins.2019.03.022
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Islam, Md. Kamrul Ahmed, Md. Manjur Kamal Z., Zamli A buffer-based online clustering for evolving data stream
description	Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hybrid online/offline, or cannot handle the property of evolving data stream. Recently, a fully online clustering algorithm for evolving data stream called CEDAS was proposed. However, similar to other density-based clustering algorithms, CEDAS requires predefining the global optimal radius of micro-clusters, which is a difficult task; in addition, an erroneous choice deteriorates cluster performance. Moreover, the algorithm ignores the presence of temporarily irrelevant micro-clusters, which may be relevant in the future. In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). This algorithm recursively updates the micro-cluster radius to its local optimal. It also introduces a buffer for storing irrelevant micro-clusters and a fully online pruning method for extracting the temporarily irrelevant micro-cluster from the buffer. In addition, BOCEDS proposes an online micro-cluster energy-updating function based on the spatial information of the data stream. Experimental results are compared with those of CEDAS and other alternative hybrid online/offline density-based clustering algorithms, and BOCEDS proves its superiority over the other clustering algorithms. The sensitivity of clustering parameters is also measured. The proposed algorithm is then applied to real-world weather data streams to demonstrate its capability to detect changes in data stream and discover arbitrarily shaped clusters. The proposed BOCEDS can be available in https://sites.google.com/view/md-manjur-ahmed and https://sites.google.com/view/kamrul-just.
format	Article
author	Islam, Md. Kamrul Ahmed, Md. Manjur Kamal Z., Zamli
author_facet	Islam, Md. Kamrul Ahmed, Md. Manjur Kamal Z., Zamli
author_sort	Islam, Md. Kamrul
title	A buffer-based online clustering for evolving data stream
title_short	A buffer-based online clustering for evolving data stream
title_full	A buffer-based online clustering for evolving data stream
title_fullStr	A buffer-based online clustering for evolving data stream
title_full_unstemmed	A buffer-based online clustering for evolving data stream
title_sort	buffer-based online clustering for evolving data stream
publisher	Elsevier Ltd
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24676/ http://umpir.ump.edu.my/id/eprint/24676/ http://umpir.ump.edu.my/id/eprint/24676/ http://umpir.ump.edu.my/id/eprint/24676/1/A%20buffer-based%20online%20clustering%20for%20evolving%20data%20stream.pdf
first_indexed	2023-09-18T22:37:29Z
last_indexed	2023-09-18T22:37:29Z
_version_	1777416693084061696

A buffer-based online clustering for evolving data stream

Similar Items