{"title":"环境监测数据挖掘的多元统计方法基础硕士导论","authors":"V. Simeonov","doi":"10.2478/cdem-2020-0002","DOIUrl":null,"url":null,"abstract":"Abstract The present introductory course of lectures summarizes the principles and algorithms of several widely used multivariate statistical methods: cluster analysis, principal components analysis, principal components regression, N-way principal components analysis, partial least squares regression and self-organizing maps with respect to their possible application in intelligent analysis, classification, modelling and interpretation to environmental monitoring data. The target group of possible users is master program students (environmental chemistry, analytical chemistry, environmental modelling and risk assessment etc.).","PeriodicalId":41079,"journal":{"name":"Chemistry-Didactics-Ecology-Metrology","volume":"25 1","pages":"35 - 56"},"PeriodicalIF":0.7000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Basic Multivariate Statistical Methods for Environmental Monitoring Data Mining: Introductory Course for Master Students\",\"authors\":\"V. Simeonov\",\"doi\":\"10.2478/cdem-2020-0002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The present introductory course of lectures summarizes the principles and algorithms of several widely used multivariate statistical methods: cluster analysis, principal components analysis, principal components regression, N-way principal components analysis, partial least squares regression and self-organizing maps with respect to their possible application in intelligent analysis, classification, modelling and interpretation to environmental monitoring data. The target group of possible users is master program students (environmental chemistry, analytical chemistry, environmental modelling and risk assessment etc.).\",\"PeriodicalId\":41079,\"journal\":{\"name\":\"Chemistry-Didactics-Ecology-Metrology\",\"volume\":\"25 1\",\"pages\":\"35 - 56\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemistry-Didactics-Ecology-Metrology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/cdem-2020-0002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemistry-Didactics-Ecology-Metrology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/cdem-2020-0002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Basic Multivariate Statistical Methods for Environmental Monitoring Data Mining: Introductory Course for Master Students
Abstract The present introductory course of lectures summarizes the principles and algorithms of several widely used multivariate statistical methods: cluster analysis, principal components analysis, principal components regression, N-way principal components analysis, partial least squares regression and self-organizing maps with respect to their possible application in intelligent analysis, classification, modelling and interpretation to environmental monitoring data. The target group of possible users is master program students (environmental chemistry, analytical chemistry, environmental modelling and risk assessment etc.).