{"title":"一种可视化的时空数据挖掘方法","authors":"Mohand Tahar Kechadi, M. Bertolotto","doi":"10.1109/IRI.2006.252465","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a system for mining very large spatio-temporal datasets. The system comprises new techniques to efficiently support the data-mining process, address the spatial and temporal dimensions of the dataset, and visualize and interpret results. In particular, we have developed an advanced visualization tool for flexible and intuitive interaction with the dataset, including functionality for displaying association rules and variable distributions","PeriodicalId":402255,"journal":{"name":"2006 IEEE International Conference on Information Reuse & Integration","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Visual Approach for Spatio-Temporal Data Mining\",\"authors\":\"Mohand Tahar Kechadi, M. Bertolotto\",\"doi\":\"10.1109/IRI.2006.252465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a system for mining very large spatio-temporal datasets. The system comprises new techniques to efficiently support the data-mining process, address the spatial and temporal dimensions of the dataset, and visualize and interpret results. In particular, we have developed an advanced visualization tool for flexible and intuitive interaction with the dataset, including functionality for displaying association rules and variable distributions\",\"PeriodicalId\":402255,\"journal\":{\"name\":\"2006 IEEE International Conference on Information Reuse & Integration\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Information Reuse & Integration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI.2006.252465\",\"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 Information Reuse & Integration","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI.2006.252465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a system for mining very large spatio-temporal datasets. The system comprises new techniques to efficiently support the data-mining process, address the spatial and temporal dimensions of the dataset, and visualize and interpret results. In particular, we have developed an advanced visualization tool for flexible and intuitive interaction with the dataset, including functionality for displaying association rules and variable distributions
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