{"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}
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.