Cited by 3,396Advanced Clustering Algorithms ResearchBayesian Methods and Mixture ModelsData Analysis with R

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

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Charrad, Malika, Ghazzali, Nadia, Boiteau, Véronique, Niknafs, Azam·2014·Journal of Statistical Software

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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.

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R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Untitled work
(1967) · Cited by 23,719
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
FISHER, R. A. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 15,227
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523
Data clustering
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925
Untitled work
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,537
Cluster Analysis
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,186

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Authors: Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A.
Affiliations: Centre d'études et de recherche en informatique et communications (FR)
ORCID: Charrad, Malika
Venue: Journal of Statistical Software
Publisher: Foundation for Open Access Statistic
Published: 2014
Volume: 61
Issue: 6
Pages: 1-36
ISSN: 1548-7660 (ISSN-L)
DOI: 10.18637/jss.v061.i06
OpenAlex: W1944603233
Cited by: 3,396
References: 50
Citations by year: 2026: 256 · 2025: 403 · 2024: 411 · 2023: 455 · 2022: 435 · 2021: 421 · 2020: 418 · 2019: 216 · 2018: 167 · 2017: 106 · 2016: 66 · 2015: 39 · 2014: 2
FWCI: 71.12
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: cc-by
Locations: Journal of Statistical Software (publishedVersion, cc-by)
HAL (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
Keywords: optimal number of clusters, cluster validity indices, cluster validation, K-means clustering, hierarchical clustering, R package, distance measures, aggregation methods

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

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