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A review on data stream classification

At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-bin...

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Main Authors: A. A, Haneen, A., Noraziah, Abd Wahab, Mohd Helmy
Format: Article
Language:English
Published: IOP Publishing 2018
Subjects:
QA75-76.95 Calculating machines
Online Access:http://eprints.uthm.edu.my/2893/1/AJ%202019%20%2861%29.pdf
http://eprints.uthm.edu.my/2893/
https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012019
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spelling my.uthm.eprints.28932021-11-16T04:00:49Z http://eprints.uthm.edu.my/2893/ A review on data stream classification A. A, Haneen A., Noraziah Abd Wahab, Mohd Helmy QA75-76.95 Calculating machines At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies. IOP Publishing 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/2893/1/AJ%202019%20%2861%29.pdf A. A, Haneen and A., Noraziah and Abd Wahab, Mohd Helmy (2018) A review on data stream classification. Journal of Physics: Conference Series (JPCS), 1018. pp. 1-8. ISSN 1742-6588 https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012019
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA75-76.95 Calculating machines
spellingShingle QA75-76.95 Calculating machines
A. A, Haneen
A., Noraziah
Abd Wahab, Mohd Helmy
A review on data stream classification
description At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies.
format Article
author A. A, Haneen
A., Noraziah
Abd Wahab, Mohd Helmy
author_facet A. A, Haneen
A., Noraziah
Abd Wahab, Mohd Helmy
author_sort A. A, Haneen
title A review on data stream classification
title_short A review on data stream classification
title_full A review on data stream classification
title_fullStr A review on data stream classification
title_full_unstemmed A review on data stream classification
title_sort review on data stream classification
publisher IOP Publishing
publishDate 2018
url http://eprints.uthm.edu.my/2893/1/AJ%202019%20%2861%29.pdf
http://eprints.uthm.edu.my/2893/
https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012019
_version_ 1738581053670424576
score 13.211869

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