{"title":"发现多流序列中的重要模式","authors":"Robert Gwadera, F. Crestani","doi":"10.1109/ICDM.2008.146","DOIUrl":null,"url":null,"abstract":"Discovering significant patterns in synchronized multi-stream sequences also known as multi-attribute event sequences (multi-sequences), is an important problem in many domains, including monitoring systems and information retrieval. In this paper we propose a new approach for assessing significance of multi-stream patterns in multi-attribute event sequences. In experiments on physiological multi-stream data we show applicability of our method.","PeriodicalId":252958,"journal":{"name":"2008 Eighth IEEE International Conference on Data Mining","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Discovering Significant Patterns in Multi-stream Sequences\",\"authors\":\"Robert Gwadera, F. Crestani\",\"doi\":\"10.1109/ICDM.2008.146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Discovering significant patterns in synchronized multi-stream sequences also known as multi-attribute event sequences (multi-sequences), is an important problem in many domains, including monitoring systems and information retrieval. In this paper we propose a new approach for assessing significance of multi-stream patterns in multi-attribute event sequences. In experiments on physiological multi-stream data we show applicability of our method.\",\"PeriodicalId\":252958,\"journal\":{\"name\":\"2008 Eighth IEEE International Conference on Data Mining\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Eighth IEEE International Conference on Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDM.2008.146\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Eighth IEEE International Conference on Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM.2008.146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Discovering Significant Patterns in Multi-stream Sequences
Discovering significant patterns in synchronized multi-stream sequences also known as multi-attribute event sequences (multi-sequences), is an important problem in many domains, including monitoring systems and information retrieval. In this paper we propose a new approach for assessing significance of multi-stream patterns in multi-attribute event sequences. In experiments on physiological multi-stream data we show applicability of our method.