{"title":"用归纳逻辑编程进行离群值检测","authors":"F. Angiulli, Fabio Fassetti","doi":"10.1109/ICDM.2009.127","DOIUrl":null,"url":null,"abstract":"We present a novel definition of outlier in the context of inductive logic programming. Given a set of positive and negative examples, the definition aims at singling out the examples showing anomalous behavior. We note that the task here pursued is different from noise removal, and, in fact, the anomalous observations we discover are different in nature from noisy ones. We discuss pecularities of the novel approach, present an algorithm for detecting outliers, discuss some examples of knowledge mined, and compare it with alternative approaches.","PeriodicalId":247645,"journal":{"name":"2009 Ninth IEEE International Conference on Data Mining","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Outlier Detection Using Inductive Logic Programming\",\"authors\":\"F. Angiulli, Fabio Fassetti\",\"doi\":\"10.1109/ICDM.2009.127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a novel definition of outlier in the context of inductive logic programming. Given a set of positive and negative examples, the definition aims at singling out the examples showing anomalous behavior. We note that the task here pursued is different from noise removal, and, in fact, the anomalous observations we discover are different in nature from noisy ones. We discuss pecularities of the novel approach, present an algorithm for detecting outliers, discuss some examples of knowledge mined, and compare it with alternative approaches.\",\"PeriodicalId\":247645,\"journal\":{\"name\":\"2009 Ninth IEEE International Conference on Data Mining\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Ninth IEEE International Conference on Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDM.2009.127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Ninth IEEE International Conference on Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM.2009.127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Outlier Detection Using Inductive Logic Programming
We present a novel definition of outlier in the context of inductive logic programming. Given a set of positive and negative examples, the definition aims at singling out the examples showing anomalous behavior. We note that the task here pursued is different from noise removal, and, in fact, the anomalous observations we discover are different in nature from noisy ones. We discuss pecularities of the novel approach, present an algorithm for detecting outliers, discuss some examples of knowledge mined, and compare it with alternative approaches.
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