{"title":"区间符号数据的规范化","authors":"Junpeng Guo, Wenhua Li, Sue Cheng","doi":"10.1109/ICIEEM.2009.5344314","DOIUrl":null,"url":null,"abstract":"As a new tool in data mining, symbolic data analysis (SDA) can not only decrease the computational complexity of huge data sets, but also master the property of the sample integrally. In many statistical analysis methods the sample data need to be normalized in advance. This paper focuses on the normalization of interval symbolic data. Firstly, on the assumption that all the individuals are uniformly distributed within the interval, the method of normalization of interval symbolic data is presented. Then, the method of normalization of interval symbolic data of arbitrary distribution is studied. For this purpose, the mean and variance of interval symbolic data is brought forward in advance. Finally, an example of evaluation on different financial stocks in Chinese stock market is given.","PeriodicalId":6326,"journal":{"name":"2009 16th International Conference on Industrial Engineering and Engineering Management","volume":"29 1","pages":"690-693"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Normalization of interval symbolic data\",\"authors\":\"Junpeng Guo, Wenhua Li, Sue Cheng\",\"doi\":\"10.1109/ICIEEM.2009.5344314\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a new tool in data mining, symbolic data analysis (SDA) can not only decrease the computational complexity of huge data sets, but also master the property of the sample integrally. In many statistical analysis methods the sample data need to be normalized in advance. This paper focuses on the normalization of interval symbolic data. Firstly, on the assumption that all the individuals are uniformly distributed within the interval, the method of normalization of interval symbolic data is presented. Then, the method of normalization of interval symbolic data of arbitrary distribution is studied. For this purpose, the mean and variance of interval symbolic data is brought forward in advance. Finally, an example of evaluation on different financial stocks in Chinese stock market is given.\",\"PeriodicalId\":6326,\"journal\":{\"name\":\"2009 16th International Conference on Industrial Engineering and Engineering Management\",\"volume\":\"29 1\",\"pages\":\"690-693\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 16th International Conference on Industrial Engineering and Engineering Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEEM.2009.5344314\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 16th International Conference on Industrial Engineering and Engineering Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEEM.2009.5344314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
As a new tool in data mining, symbolic data analysis (SDA) can not only decrease the computational complexity of huge data sets, but also master the property of the sample integrally. In many statistical analysis methods the sample data need to be normalized in advance. This paper focuses on the normalization of interval symbolic data. Firstly, on the assumption that all the individuals are uniformly distributed within the interval, the method of normalization of interval symbolic data is presented. Then, the method of normalization of interval symbolic data of arbitrary distribution is studied. For this purpose, the mean and variance of interval symbolic data is brought forward in advance. Finally, an example of evaluation on different financial stocks in Chinese stock market is given.