{"title":"图像分析分类中的聚类划分:一种遗传算法方法","authors":"C. Alippi, R. Cucchiara","doi":"10.1109/CMPEUR.1992.218520","DOIUrl":null,"url":null,"abstract":"A classification of data by using the genetic algorithm computational paradigm is proposed. The best data partition is defined to be the one minimizing the sum of Pythagorean distances between each datum in a cluster and the relative center of class or center of mass. Background is given, and the relevant genetic algorithm description is provided. The model for the genetic application is presented. Simulation results confirm genetic algorithms to be powerful tools for the solution of optimization problems.<<ETX>>","PeriodicalId":390273,"journal":{"name":"CompEuro 1992 Proceedings Computer Systems and Software Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Cluster partitioning in image analysis classification: a genetic algorithm approach\",\"authors\":\"C. Alippi, R. Cucchiara\",\"doi\":\"10.1109/CMPEUR.1992.218520\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A classification of data by using the genetic algorithm computational paradigm is proposed. The best data partition is defined to be the one minimizing the sum of Pythagorean distances between each datum in a cluster and the relative center of class or center of mass. Background is given, and the relevant genetic algorithm description is provided. The model for the genetic application is presented. Simulation results confirm genetic algorithms to be powerful tools for the solution of optimization problems.<<ETX>>\",\"PeriodicalId\":390273,\"journal\":{\"name\":\"CompEuro 1992 Proceedings Computer Systems and Software Engineering\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CompEuro 1992 Proceedings Computer Systems and Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CMPEUR.1992.218520\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CompEuro 1992 Proceedings Computer Systems and Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMPEUR.1992.218520","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cluster partitioning in image analysis classification: a genetic algorithm approach
A classification of data by using the genetic algorithm computational paradigm is proposed. The best data partition is defined to be the one minimizing the sum of Pythagorean distances between each datum in a cluster and the relative center of class or center of mass. Background is given, and the relevant genetic algorithm description is provided. The model for the genetic application is presented. Simulation results confirm genetic algorithms to be powerful tools for the solution of optimization problems.<>
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