{"title":"基于k均值聚类和马尔可夫模型的建筑维修传感器数据离群度估计","authors":"K. Aoki","doi":"10.4156/AISS.VOL5.ISSUE7.108","DOIUrl":null,"url":null,"abstract":"There are many sensors in a building. Those sensors gather huge amount of various data in every hour. The data must show some failures in the building. However, the amount of data prevents from utilizing the sign. The variety of the sensors makes difficult to uniform processing over all data. This paper discusses the uniform processing method over various sensor data in buildings using K-means clustering and Markov model.","PeriodicalId":247895,"journal":{"name":"The 3rd International Conference on Data Mining and Intelligent Information Technology Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Outlier degree estimation in various sensor data for building maintenance using K-means clustering and Markov model\",\"authors\":\"K. Aoki\",\"doi\":\"10.4156/AISS.VOL5.ISSUE7.108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are many sensors in a building. Those sensors gather huge amount of various data in every hour. The data must show some failures in the building. However, the amount of data prevents from utilizing the sign. The variety of the sensors makes difficult to uniform processing over all data. This paper discusses the uniform processing method over various sensor data in buildings using K-means clustering and Markov model.\",\"PeriodicalId\":247895,\"journal\":{\"name\":\"The 3rd International Conference on Data Mining and Intelligent Information Technology Applications\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 3rd International Conference on Data Mining and Intelligent Information Technology Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4156/AISS.VOL5.ISSUE7.108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 3rd International Conference on Data Mining and Intelligent Information Technology Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4156/AISS.VOL5.ISSUE7.108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Outlier degree estimation in various sensor data for building maintenance using K-means clustering and Markov model
There are many sensors in a building. Those sensors gather huge amount of various data in every hour. The data must show some failures in the building. However, the amount of data prevents from utilizing the sign. The variety of the sensors makes difficult to uniform processing over all data. This paper discusses the uniform processing method over various sensor data in buildings using K-means clustering and Markov model.
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