Victor Hugo Silva-Blancas , José Manuel Álvarez-Alvarado , Hugo Jiménez-Hernández , Ana Marcela Herrera-Navarro , Diana Margarita Córdova-Esparza , Juvenal Rodríguez-Reséndiz
{"title":"Infinite Type Centroid java library: An implementation of parameterized coordinates for an enhanced centroid calculation during K-means classification","authors":"Victor Hugo Silva-Blancas , José Manuel Álvarez-Alvarado , Hugo Jiménez-Hernández , Ana Marcela Herrera-Navarro , Diana Margarita Córdova-Esparza , Juvenal Rodríguez-Reséndiz","doi":"10.1016/j.simpa.2025.100751","DOIUrl":null,"url":null,"abstract":"<div><div>InfiniteTypeCentroid presents a theorem design to enhance centroid calculation on the K-means algorithm by integrating a parameters list that produces hidden information and offers improved results choose in the course of research. It shows a capacity for universalization and adaptability to address any mathematical analysis and it can be used in any Java compiler a parent class. Data analysis on use cases results in 18.22% improved accuracy and data behavior for a specific dataverse produces seven tendency charts with enriched significance. Improves data structures by debugging non-significant values and offering an improved methodology for hypothesis definition.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100751"},"PeriodicalIF":1.3000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963825000119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
InfiniteTypeCentroid presents a theorem design to enhance centroid calculation on the K-means algorithm by integrating a parameters list that produces hidden information and offers improved results choose in the course of research. It shows a capacity for universalization and adaptability to address any mathematical analysis and it can be used in any Java compiler a parent class. Data analysis on use cases results in 18.22% improved accuracy and data behavior for a specific dataverse produces seven tendency charts with enriched significance. Improves data structures by debugging non-significant values and offering an improved methodology for hypothesis definition.