{"title":"Diagonally Colorized iVAT Images for Labeled Data","authors":"Elizabeth D. Hathaway, R. Hathaway","doi":"10.1109/ICDMW58026.2022.00043","DOIUrl":null,"url":null,"abstract":"The iVAT (improved Visual Assessment of cluster Tendency) image is a useful tool for assessing possible cluster structure in an unlabeled, numerical data set. If labeled data are available then it is sometimes helpful to determine how closely the (unlabeled) data clusters agree with the data partitioning based on the labels. In this note the DCiVAT (Diagonally Colorized iVAT) image is introduced for the case of labeled data. It incorporates all available data and label information into a single colorized iVAT image so that it is possible to visually assess the degree to which data clusters are aligned with label categories. The new approach is illustrated with several examples.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW58026.2022.00043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The iVAT (improved Visual Assessment of cluster Tendency) image is a useful tool for assessing possible cluster structure in an unlabeled, numerical data set. If labeled data are available then it is sometimes helpful to determine how closely the (unlabeled) data clusters agree with the data partitioning based on the labels. In this note the DCiVAT (Diagonally Colorized iVAT) image is introduced for the case of labeled data. It incorporates all available data and label information into a single colorized iVAT image so that it is possible to visually assess the degree to which data clusters are aligned with label categories. The new approach is illustrated with several examples.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
