NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set

Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequ...

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Vydáno v:	Journal of statistical software Ročník 61; číslo 6; s. 1 - 36
Hlavní autoři:	Charrad, Malika, Ghazzali, Nadia, Boiteau, Véronique, Niknafs, Azam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	University of California, Los Angeles 2014 Foundation for Open Access Statistics
Témata:	Computer Science Mathematics Statistics Mise en cluster Progiciel R Cluster validity indices Clustering Programmation de R R package Indices de validité cluster R programming
ISSN:	1548-7660, 1548-7660
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity.The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them.The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform k-means and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the data set of interest.
AbstractList	Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity. The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them. The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform k-means and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the data set of interest.
Author	Niknafs, Azam Charrad, Malika Ghazzali, Nadia Boiteau, Véronique
Author_xml	– sequence: 1 givenname: Malika surname: Charrad fullname: Charrad, Malika – sequence: 2 givenname: Nadia surname: Ghazzali fullname: Ghazzali, Nadia – sequence: 3 givenname: Véronique surname: Boiteau fullname: Boiteau, Véronique – sequence: 4 givenname: Azam surname: Niknafs fullname: Niknafs, Azam
BackLink	https://hal.science/hal-01126479$$DView record in HAL
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Title	NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set
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