clusEvol: An R package for Cluster Evolution Analytics
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024002917 |
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| author | Víctor Morales-Oñate Bolívar Morales-Oñate |
| author_facet | Víctor Morales-Oñate Bolívar Morales-Oñate |
| author_sort | Víctor Morales-Oñate |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3bdae4653e4746e7b923214f5b0e876e |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-3bdae4653e4746e7b923214f5b0e876e2025-08-20T01:54:15ZengElsevierSoftwareX2352-71102024-12-012810192110.1016/j.softx.2024.101921clusEvol: An R package for Cluster Evolution AnalyticsVíctor Morales-Oñate0Bolívar Morales-Oñate1Universidad de las Américas, Departamento de Economía, Quito, Ecuador; Corresponding author.Escuela Politécnica Superior de Chimborazo, Grupo de Investigación Data Science Research Group, Riobamba, EcuadorThe 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.http://www.sciencedirect.com/science/article/pii/S2352711024002917Exploratory data analysisUnsupervised learningClusteringStatistics |
| spellingShingle | Víctor Morales-Oñate Bolívar Morales-Oñate clusEvol: An R package for Cluster Evolution Analytics SoftwareX Exploratory data analysis Unsupervised learning Clustering Statistics |
| title | clusEvol: An R package for Cluster Evolution Analytics |
| title_full | clusEvol: An R package for Cluster Evolution Analytics |
| title_fullStr | clusEvol: An R package for Cluster Evolution Analytics |
| title_full_unstemmed | clusEvol: An R package for Cluster Evolution Analytics |
| title_short | clusEvol: An R package for Cluster Evolution Analytics |
| title_sort | clusevol an r package for cluster evolution analytics |
| topic | Exploratory data analysis Unsupervised learning Clustering Statistics |
| url | http://www.sciencedirect.com/science/article/pii/S2352711024002917 |
| work_keys_str_mv | AT victormoralesonate clusevolanrpackageforclusterevolutionanalytics AT bolivarmoralesonate clusevolanrpackageforclusterevolutionanalytics |