{"title":"从临床数据库中学习和发现的增量概念形成方法","authors":"V. Soo, Jan-Sing Wang, Shih-Pu Wang","doi":"10.1109/ICNN.1994.374706","DOIUrl":null,"url":null,"abstract":"The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, modified from Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class membership and the index-conjunction class membership. The former is a polythetic clustering approach that serves at the early stage of concept formation. The latter that allows only relevant instances to be placed in the same cluster serves as the later stage of concept formation. D-UNIMEM could extract interesting correlation among features from the learned concept hierarchy.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An incremental concept formation approach to learn and discover from a clinical database\",\"authors\":\"V. Soo, Jan-Sing Wang, Shih-Pu Wang\",\"doi\":\"10.1109/ICNN.1994.374706\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, modified from Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class membership and the index-conjunction class membership. The former is a polythetic clustering approach that serves at the early stage of concept formation. The latter that allows only relevant instances to be placed in the same cluster serves as the later stage of concept formation. D-UNIMEM could extract interesting correlation among features from the learned concept hierarchy.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374706\",\"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 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374706","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An incremental concept formation approach to learn and discover from a clinical database
The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, modified from Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class membership and the index-conjunction class membership. The former is a polythetic clustering approach that serves at the early stage of concept formation. The latter that allows only relevant instances to be placed in the same cluster serves as the later stage of concept formation. D-UNIMEM could extract interesting correlation among features from the learned concept hierarchy.<>
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