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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2893/1/AJ%202019%20%2861%29.pdf |
| _version_ | 1796868735691128832 |
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| 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 |
| collection | UTHM |
| 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. |
| first_indexed | 2024-03-05T21:44:15Z |
| format | Article |
| id | uthm.eprints-2893 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:44:15Z |
| publishDate | 2018 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | 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 |
| spellingShingle | QA75-76.95 Calculating machines A. A, Haneen A., Noraziah Abd Wahab, Mohd Helmy A review on data stream classification |
| title | 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_short | A review on data stream classification |
| title_sort | review on data stream classification |
| topic | QA75-76.95 Calculating machines |
| url | http://eprints.uthm.edu.my/2893/1/AJ%202019%20%2861%29.pdf |
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