J. Han, Yongheng Fu, Wen Wang, Jenny Chiang, Osmar R Zaiane, K. Koperski
{"title":"DBMiner","authors":"J. Han, Yongheng Fu, Wen Wang, Jenny Chiang, Osmar R Zaiane, K. Koperski","doi":"10.1145/233269.280356","DOIUrl":null,"url":null,"abstract":"Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.","PeriodicalId":101610,"journal":{"name":"Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"41","resultStr":"{\"title\":\"DBMiner\",\"authors\":\"J. Han, Yongheng Fu, Wen Wang, Jenny Chiang, Osmar R Zaiane, K. Koperski\",\"doi\":\"10.1145/233269.280356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.\",\"PeriodicalId\":101610,\"journal\":{\"name\":\"Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"41\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/233269.280356\",\"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 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/233269.280356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.