{"title":"基于k均值算法的气象数据分析","authors":"Jinghua Huang, Zhenchong Wang, Mei Yuan, Y. Bao","doi":"10.1109/ISCID.2009.164","DOIUrl":null,"url":null,"abstract":"The paper proposed a clustering method of decade observation data based on k-means algorithm, which adjusted the weight influence to similarity function by the missing values handling and scaling of range fields. This paper discussed the way to select initial cluster centers and the process of calculating cluster centers and assigning records to clusters. The test indicated the k-means algorithm had effective clustering result.","PeriodicalId":294370,"journal":{"name":"International Symposium on Computational Intelligence and Design","volume":"17 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Meteorological Data Analyze Base on K-means Algorithm\",\"authors\":\"Jinghua Huang, Zhenchong Wang, Mei Yuan, Y. Bao\",\"doi\":\"10.1109/ISCID.2009.164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposed a clustering method of decade observation data based on k-means algorithm, which adjusted the weight influence to similarity function by the missing values handling and scaling of range fields. This paper discussed the way to select initial cluster centers and the process of calculating cluster centers and assigning records to clusters. The test indicated the k-means algorithm had effective clustering result.\",\"PeriodicalId\":294370,\"journal\":{\"name\":\"International Symposium on Computational Intelligence and Design\",\"volume\":\"17 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Symposium on Computational Intelligence and Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCID.2009.164\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2009.164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Meteorological Data Analyze Base on K-means Algorithm
The paper proposed a clustering method of decade observation data based on k-means algorithm, which adjusted the weight influence to similarity function by the missing values handling and scaling of range fields. This paper discussed the way to select initial cluster centers and the process of calculating cluster centers and assigning records to clusters. The test indicated the k-means algorithm had effective clustering result.