{"title":"Neighborgram集群。集群社区的互动探索","authors":"M. Berthold, Bernd Wiswedel, D. E. Patterson","doi":"10.1109/ICDM.2002.1184004","DOIUrl":null,"url":null,"abstract":"We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize, select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically, allowing for an automatic or semi-automatic clustering process where the user only occasionally interacts with the algorithm. We illustrate the ability to automatically identify and visualize clusters using NCI's AIDS Antiviral Screen data set.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"170 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Neighborgram clustering. Interactive exploration of cluster neighborhoods\",\"authors\":\"M. Berthold, Bernd Wiswedel, D. E. Patterson\",\"doi\":\"10.1109/ICDM.2002.1184004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize, select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically, allowing for an automatic or semi-automatic clustering process where the user only occasionally interacts with the algorithm. We illustrate the ability to automatically identify and visualize clusters using NCI's AIDS Antiviral Screen data set.\",\"PeriodicalId\":405340,\"journal\":{\"name\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"volume\":\"170 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"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.2002.1184004\",\"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.2002.1184004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neighborgram clustering. Interactive exploration of cluster neighborhoods
We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize, select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically, allowing for an automatic or semi-automatic clustering process where the user only occasionally interacts with the algorithm. We illustrate the ability to automatically identify and visualize clusters using NCI's AIDS Antiviral Screen data set.