Graham J. Williams, R. Baxter, Hongxing He, S. Hawkins, Lifang Gu
{"title":"RNN在数据挖掘异常点检测中的比较研究","authors":"Graham J. Williams, R. Baxter, Hongxing He, S. Hawkins, Lifang Gu","doi":"10.1109/ICDM.2002.1184035","DOIUrl":null,"url":null,"abstract":"We have proposed replicator neural networks (RNNs) for outlier detection. We compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets in particular test scalability and practicality of application.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"292","resultStr":"{\"title\":\"A comparative study of RNN for outlier detection in data mining\",\"authors\":\"Graham J. Williams, R. Baxter, Hongxing He, S. Hawkins, Lifang Gu\",\"doi\":\"10.1109/ICDM.2002.1184035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have proposed replicator neural networks (RNNs) for outlier detection. We compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets in particular test scalability and practicality of application.\",\"PeriodicalId\":405340,\"journal\":{\"name\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"292\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDM.2002.1184035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM.2002.1184035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparative study of RNN for outlier detection in data mining
We have proposed replicator neural networks (RNNs) for outlier detection. We compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets in particular test scalability and practicality of application.