{"title":"使用自组织地图和数据驱动的颜色映射的无监督可视化数据挖掘","authors":"Cyril de Runz, É. Desjardin, M. Herbin","doi":"10.1109/IV.2012.48","DOIUrl":null,"url":null,"abstract":"This paper presents a new approach for visually mining multivariate datasets and especially large ones. This unsupervised approach proposes to mix a SOM approach and a pixel-oriented visualization. The map is considered as a set of connected pixels, the space filling is driven by the SOM algorithm, and the color of each pixel is computed directly from data using an approach proposed by Blanchard et al. The method visually summarizes the data and helps in understanding its inner structure.","PeriodicalId":264951,"journal":{"name":"2012 16th International Conference on Information Visualisation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"Unsupervised Visual Data Mining Using Self-organizing Maps and a Data-driven Color Mapping\",\"authors\":\"Cyril de Runz, É. Desjardin, M. Herbin\",\"doi\":\"10.1109/IV.2012.48\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new approach for visually mining multivariate datasets and especially large ones. This unsupervised approach proposes to mix a SOM approach and a pixel-oriented visualization. The map is considered as a set of connected pixels, the space filling is driven by the SOM algorithm, and the color of each pixel is computed directly from data using an approach proposed by Blanchard et al. The method visually summarizes the data and helps in understanding its inner structure.\",\"PeriodicalId\":264951,\"journal\":{\"name\":\"2012 16th International Conference on Information Visualisation\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 16th International Conference on Information Visualisation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IV.2012.48\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 16th International Conference on Information Visualisation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IV.2012.48","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unsupervised Visual Data Mining Using Self-organizing Maps and a Data-driven Color Mapping
This paper presents a new approach for visually mining multivariate datasets and especially large ones. This unsupervised approach proposes to mix a SOM approach and a pixel-oriented visualization. The map is considered as a set of connected pixels, the space filling is driven by the SOM algorithm, and the color of each pixel is computed directly from data using an approach proposed by Blanchard et al. The method visually summarizes the data and helps in understanding its inner structure.
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