{"title":"时态Web使用挖掘","authors":"Mofreh A. Hogo, M. Snorek, P. Lingras","doi":"10.1109/WI.2003.1241237","DOIUrl":null,"url":null,"abstract":"Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal patterns, which describe the temporal behavior of Web users. Clusters and associations in Web usage mining do not necessarily have crisp boundaries. We introduce the temporal Web usage mining of Web users on one educational Web site, using the adapted Kohonen SOM based on rough set properties [L. J. Pawan et al. (2002)].","PeriodicalId":403574,"journal":{"name":"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Temporal Web usage mining\",\"authors\":\"Mofreh A. Hogo, M. Snorek, P. Lingras\",\"doi\":\"10.1109/WI.2003.1241237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal patterns, which describe the temporal behavior of Web users. Clusters and associations in Web usage mining do not necessarily have crisp boundaries. We introduce the temporal Web usage mining of Web users on one educational Web site, using the adapted Kohonen SOM based on rough set properties [L. J. Pawan et al. (2002)].\",\"PeriodicalId\":403574,\"journal\":{\"name\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2003.1241237\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2003.1241237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
摘要
时态Web使用挖掘涉及到数据挖掘技术在时态Web使用数据上的应用,以发现描述Web用户时态行为的时态模式。Web使用挖掘中的集群和关联不一定有明确的边界。本文采用基于粗糙集属性的Kohonen SOM对某教育网站的Web用户进行时态Web使用挖掘。J. Pawan et al.(2002)。
Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal patterns, which describe the temporal behavior of Web users. Clusters and associations in Web usage mining do not necessarily have crisp boundaries. We introduce the temporal Web usage mining of Web users on one educational Web site, using the adapted Kohonen SOM based on rough set properties [L. J. Pawan et al. (2002)].