时间重加权狄利克雷过程混合异常检测器

JunYong Tong, Nick Torenvliet
{"title":"时间重加权狄利克雷过程混合异常检测器","authors":"JunYong Tong, Nick Torenvliet","doi":"10.1109/ICDMW51313.2020.00045","DOIUrl":null,"url":null,"abstract":"This paper proposes a streaming anomaly detection algorithm using variational Bayesian non-parametric methods. We extend the use of Dirichlet process mixture models to anomaly detection for online streaming data through the use of streaming variational bayes method and a cohesion function. Using our algorithm, we were able to update model parameters sequentially near real-time, using a fixed amount of computational resources. The algorithm was able to capture the temporal dynamics of the data and enabled good online anomaly detection. We demonstrate the performance, and discuss results, of the algorithm on an industrial datasets with anomalies provided by a local utility.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector\",\"authors\":\"JunYong Tong, Nick Torenvliet\",\"doi\":\"10.1109/ICDMW51313.2020.00045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a streaming anomaly detection algorithm using variational Bayesian non-parametric methods. We extend the use of Dirichlet process mixture models to anomaly detection for online streaming data through the use of streaming variational bayes method and a cohesion function. Using our algorithm, we were able to update model parameters sequentially near real-time, using a fixed amount of computational resources. The algorithm was able to capture the temporal dynamics of the data and enabled good online anomaly detection. We demonstrate the performance, and discuss results, of the algorithm on an industrial datasets with anomalies provided by a local utility.\",\"PeriodicalId\":426846,\"journal\":{\"name\":\"2020 International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW51313.2020.00045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW51313.2020.00045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种基于变分贝叶斯非参数方法的流异常检测算法。我们通过使用流变分贝叶斯方法和内聚函数将Dirichlet过程混合模型的使用扩展到在线流数据的异常检测中。使用我们的算法,我们能够使用固定数量的计算资源,近乎实时地顺序更新模型参数。该算法能够捕获数据的时间动态,并实现良好的在线异常检测。我们在本地公用事业公司提供的具有异常的工业数据集上演示了该算法的性能并讨论了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector
This paper proposes a streaming anomaly detection algorithm using variational Bayesian non-parametric methods. We extend the use of Dirichlet process mixture models to anomaly detection for online streaming data through the use of streaming variational bayes method and a cohesion function. Using our algorithm, we were able to update model parameters sequentially near real-time, using a fixed amount of computational resources. The algorithm was able to capture the temporal dynamics of the data and enabled good online anomaly detection. We demonstrate the performance, and discuss results, of the algorithm on an industrial datasets with anomalies provided by a local utility.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信