{"title":"自适应k值k均值聚类算法","authors":"Wang Shenghui, Liang Hanbing","doi":"10.1109/ICMCCE51767.2020.00316","DOIUrl":null,"url":null,"abstract":"Based on particle swarm optimization (PSO), the algorithm for selecting appropriate K values is improved by combining k-mean algorithm. When the algorithm converges, the expansion and reduction of K value can be determined by comparing the relationship between different K value selection and global optimal fitness. Experiments show that the improved algorithm can assist K value selection effectively and obtain a better clustering effect.","PeriodicalId":6712,"journal":{"name":"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)","volume":"9 1","pages":"1442-1445"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Adaptive K-valued K-means clustering algorithm\",\"authors\":\"Wang Shenghui, Liang Hanbing\",\"doi\":\"10.1109/ICMCCE51767.2020.00316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on particle swarm optimization (PSO), the algorithm for selecting appropriate K values is improved by combining k-mean algorithm. When the algorithm converges, the expansion and reduction of K value can be determined by comparing the relationship between different K value selection and global optimal fitness. Experiments show that the improved algorithm can assist K value selection effectively and obtain a better clustering effect.\",\"PeriodicalId\":6712,\"journal\":{\"name\":\"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)\",\"volume\":\"9 1\",\"pages\":\"1442-1445\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMCCE51767.2020.00316\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMCCE51767.2020.00316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Based on particle swarm optimization (PSO), the algorithm for selecting appropriate K values is improved by combining k-mean algorithm. When the algorithm converges, the expansion and reduction of K value can be determined by comparing the relationship between different K value selection and global optimal fitness. Experiments show that the improved algorithm can assist K value selection effectively and obtain a better clustering effect.