Clustering Iris Data Set
- kaggle.com
Updated Sep 2, 2023
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EmailClick to copy linkLink copiedCiteRifki Ilham (2023). Clustering Iris Data Set [Dataset]. https://www.kaggle.com/datasets/rifkiilham/clustering-iris-data-setCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedSep 2, 2023AuthorsRifki IlhamDescriptionThe Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. Please use this data set to clustering the iris flowers data. You can use k-means clustering algorithm.
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Consistency of variables in the VKFCM-K-LP clustering with the imputation of...
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xlsUpdated Jun 2, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteAnny K. G. Rodrigues; Raydonal Ospina; Marcelo R. P. Ferreira (2023). Consistency of variables in the VKFCM-K-LP clustering with the imputation of missing values using mean values for the Iris Plant dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0259266.t013xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0259266.t013Dataset updatedJun 2, 2023Dataset provided byPLOS ONEAuthorsAnny K. G. Rodrigues; Raydonal Ospina; Marcelo R. P. FerreiraLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionConsistency of variables in the VKFCM-K-LP clustering with the imputation of missing values using mean values for the Iris Plant dataset.
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FacebookTwitterEmailThe Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. Please use this data set to clustering the iris flowers data. You can use k-means clustering algorithm.