Please use this identifier to cite or link to this item:
http://dspace2020.uniten.edu.my:8080/handle/123456789/21228
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Muruti G. | en_US |
dc.contributor.author | Rahim F.A. | en_US |
dc.contributor.author | Bin Ibrahim Z.-A. | en_US |
dc.date.accessioned | 2021-09-03T03:25:37Z | - |
dc.date.available | 2021-09-03T03:25:37Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://dspace2020.uniten.edu.my:8080/handle/123456789/21228 | - |
dc.description.abstract | Dynamic research area has been applied and researched on anomaly detection in various domains. And various techniques have been proposed to identify unexpected items or events in datasets which differ from the norm. This review tries to provide a basic and structured overview of the anomaly detection techniques. Also, this review discusses major anomaly detection techniques using statistical based and machine learning based techniques. The outcome of this review may facilitate a better understanding of the different techniques in which research has been done on this topic by comparing the pros and cons of the identified techniques. In addition, this review also discusses on the measurement methods used by other researchers in validating their anomalies detection techniques. © 2018 IEEE | en_US |
dc.language.iso | en | en_US |
dc.title | A survey on anomalies detection techniques and measurement methods | en_US |
dc.type | conference paper | en_US |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.grantfulltext | reserved | - |
item.openairetype | conference paper | - |
Appears in Collections: | UNITEN Ebook and Article |
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This document is not yet available.pdf Restricted Access | 396.12 kB | Adobe PDF | View/Open Request a copy |
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