{"title":"clusEvol: An R package for Cluster Evolution Analytics","authors":"Víctor Morales-Oñate , Bolívar Morales-Oñate","doi":"10.1016/j.softx.2024.101921","DOIUrl":null,"url":null,"abstract":"<div><div>The paper proposes a new R package, named <em>clusEvol</em>, that introduces Cluster Evolution Analytics (CEA), a framework for advanced Exploratory Data Analysis and Unsupervised Learning. CEA studies the evolution of an object and its neighbors, identified via clustering algorithms, over time. It combines leave-one-out and plug-in principles, enabling “what if” scenarios by integrating current data into past datasets to explore temporal changes. The framework is demonstrated with a real dataset employing various clustering algorithms.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101921"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024002917","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
The paper proposes a new R package, named clusEvol, that introduces Cluster Evolution Analytics (CEA), a framework for advanced Exploratory Data Analysis and Unsupervised Learning. CEA studies the evolution of an object and its neighbors, identified via clustering algorithms, over time. It combines leave-one-out and plug-in principles, enabling “what if” scenarios by integrating current data into past datasets to explore temporal changes. The framework is demonstrated with a real dataset employing various clustering algorithms.
期刊介绍：
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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