{"title":"数据库重排遗传网络规划在交通预测中的后向关联规则挖掘","authors":"Huiyu Zhou, S. Mabu, K. Shimada, K. Hirasawa","doi":"10.1145/1569901.1570223","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement\",\"authors\":\"Huiyu Zhou, S. Mabu, K. Shimada, K. Hirasawa\",\"doi\":\"10.1145/1569901.1570223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.\",\"PeriodicalId\":193093,\"journal\":{\"name\":\"Proceedings of the 11th Annual conference on Genetic and evolutionary computation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 11th Annual conference on Genetic and evolutionary computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1569901.1570223\",\"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 of the 11th Annual conference on Genetic and evolutionary computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1569901.1570223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement
In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.
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