{"title":"时空环境下的罕见事件检测","authors":"Yusong. Meng, M. Dunham, M. Marchetti, Jie Huang","doi":"10.1109/GRC.2006.1635881","DOIUrl":null,"url":null,"abstract":"In this paper we explore the use of Extensible Markov Models (EMM) to detect rare events in a spatiotemporal environment. This initial work shows that an EMM is scalable, dynamic, and can detect rare events based on spatial values, temporal values, or transitions between real world states. The core of the EMM approach is a combination of clustering and dynamic Markov Chain. Keywords—Extensible Markov Model (EMM), rare event","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Rare Event Detection in a Spatiotemporal Environment\",\"authors\":\"Yusong. Meng, M. Dunham, M. Marchetti, Jie Huang\",\"doi\":\"10.1109/GRC.2006.1635881\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we explore the use of Extensible Markov Models (EMM) to detect rare events in a spatiotemporal environment. This initial work shows that an EMM is scalable, dynamic, and can detect rare events based on spatial values, temporal values, or transitions between real world states. The core of the EMM approach is a combination of clustering and dynamic Markov Chain. Keywords—Extensible Markov Model (EMM), rare event\",\"PeriodicalId\":400997,\"journal\":{\"name\":\"2006 IEEE International Conference on Granular Computing\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Granular Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GRC.2006.1635881\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Granular Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRC.2006.1635881","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rare Event Detection in a Spatiotemporal Environment
In this paper we explore the use of Extensible Markov Models (EMM) to detect rare events in a spatiotemporal environment. This initial work shows that an EMM is scalable, dynamic, and can detect rare events based on spatial values, temporal values, or transitions between real world states. The core of the EMM approach is a combination of clustering and dynamic Markov Chain. Keywords—Extensible Markov Model (EMM), rare event
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