{"title":"近期音乐查询流的在线挖掘","authors":"Hua-Fu Li, Chin-Chuan Ho, M. Shan, Suh-Yin Lee","doi":"10.1109/ICME.2006.262948","DOIUrl":null,"url":null,"abstract":"Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once","PeriodicalId":339258,"journal":{"name":"2006 IEEE International Conference on Multimedia and Expo","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Online Mining of Recent Music Query Streams\",\"authors\":\"Hua-Fu Li, Chin-Chuan Ho, M. Shan, Suh-Yin Lee\",\"doi\":\"10.1109/ICME.2006.262948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once\",\"PeriodicalId\":339258,\"journal\":{\"name\":\"2006 IEEE International Conference on Multimedia and Expo\",\"volume\":\"95 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Multimedia and Expo\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICME.2006.262948\",\"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 Multimedia and Expo","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2006.262948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once
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