NbClust : An R Package for Determining the Relevant Number of Clusters in a Data Set
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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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Convergent evidence from silhouette width, elbow and gap statistics supported a three-cluster solution that balanced cohesion and separation.
“Convergent evidence from silhouette width, elbow and gap statistics supported a three-cluster solution that balanced cohesion and separation (Charrad et al., 2014; Rousseeuw, 1987).”
From A tridimensional model of AI literacy: An empirical analysis of student performance and demographic patterns in higher education (2025) · cites this paper as Charrad et al., 2014
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
(1967) · Cited by 23,719 · see citing works
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 · see citing works
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 · see citing works
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works
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 · see citing works
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,532 · see citing works
Pattern classification and scene analysis
Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,537 · see citing works
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,186 · see citing works
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145 · see citing works
A Dendrite Method for Cluster Analysis
Caliński, T., & Harabasz, J. (1974) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610927408827101 · Cited by 7,857 · see citing works
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205 · see citing works
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958 · see citing works
A Statistical Method for Evaluating Systematic Relationships
Sokal, R. R., & Michener, C. D. (1958) · Biodiversity Heritage Library (Smithsonian Institution) · doi:10.5281/zenodo.16435756 · Cited by 4,432 · see citing works
Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
Murtagh, F. D., & Legendre, P. (2014) · Journal of Classification · doi:10.1007/s00357-014-9161-z · Cited by 3,939 · see citing works
An Examination of Procedures for Determining the Number of Clusters in a Data Set
Milligan, G. W., & Cooper, M. C. (1985) · Psychometrika · doi:10.1007/bf02294245 · Cited by 3,891 · see citing works
On Clustering Validation Techniques
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001) · Journal of Intelligent Information Systems · doi:10.1023/a:1012801612483 · Cited by 2,651 · see citing works
Well-Separated Clusters and Optimal Fuzzy Partitions
Dunn, J. C. (1974) · Journal of Cybernetics · doi:10.1080/01969727408546059 · Cited by 2,560 · see citing works
Sørensen, T. B., Sorensen, T., Biering-Sørensen, T., Ti, S., & Sorensen, J. (1948) · Medical Entomology and Zoology · Cited by 2,038 · see citing works
An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms
Milligan, G. W. (1980) · Psychometrika · doi:10.1007/bf02293907 · Cited by 1,176 · see citing works
Some new indexes of cluster validity
Bezdek, J. C., & Pal, N. R. (1998) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.678624 · Cited by 1,147 · see citing works
Statistique exploratoire multidimensionnelle
Lebart, L., Morineau, A., & Piron, M. (1995) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,051 · see citing works
Saisana, M., Saltelli, A., & Tarantola, S. (2005) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2005.00350.x · Cited by 1,015 · see citing works
ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION
Ball, G. H., & Hall, D. J. (1965) · Defense Technical Information Center (DTIC) · Cited by 919 · see citing works
clValid : An R Package for Cluster Validation
Brock, G. N., Pihur, V., Datta, S., & Datta, S. (2008) · Journal of Statistical Software · doi:10.18637/jss.v025.i04 · Cited by 879 · see citing works
A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering
Krzanowski, W. J., & Lai, Y. T. (1988) · Biometrics · doi:10.2307/2531893 · Cited by 718 · see citing works
On Some Invariant Criteria for Grouping Data
Friedman, H. P., & Rubin, J. (1967) · Journal of the American Statistical Association · doi:10.1080/01621459.1967.10500923 · Cited by 613 · see citing works
Edwards, A. W. F., & Cavalli-Sforza, L. L. (1965) · Biometrics · doi:10.2307/2528096 · Cited by 597 · see citing works
Gordon, A. (1999) · Monographs on statistics and applied probability · doi:10.1201/9781584888536 · Cited by 515 · see citing works
Clustering Methods Based on Likelihood Ratio Criteria
Scott, A. J., & Symons, M. (1971) · Biometrics · doi:10.2307/2529003 · Cited by 468 · see citing works
Clustering validity assessment: finding the optimal partitioning of a data set
Halkidi, M., & Vazirgiannis, M. (2002) · Proceedings · doi:10.1109/icdm.2001.989517 · Cited by 433 · see citing works
An Agglomerative Method for Classification of Plant Communities
Orlóci, L. (1967) · Journal of Ecology · doi:10.2307/2257725 · Cited by 427 · see citing works
A Comparison of Some Methods of Cluster Analysis
Gower, J. C. (1967) · Biometrics · doi:10.2307/2528417 · Cited by 411 · see citing works
A Monte Carlo Study of Thirty Internal Criterion Measures for Cluster Analysis
Milligan, G. W. (1981) · Psychometrika · doi:10.1007/bf02293899 · Cited by 409 · see citing works
A general statistical framework for assessing categorical clustering in free recall.
Hubert, L. J., & Levin, J. R. (1976) · Psychological Bulletin · doi:10.1037//0033-2909.83.6.1072 · Cited by 398 · see citing works
Quality Scheme Assessment in the Clustering Process
Halkidi, M., Vazirgiannis, M., & Batistakis, Y. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45372-5_26 · Cited by 306 · see citing works
An Examination of Indexes for Determining the Number of Clusters in Binary Data Sets
Dimitriadou, E., Dolničar, S., & Weingessel, A. (2002) · Psychometrika · doi:10.1007/bf02294713 · Cited by 301 · see citing works
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
Sheikholeslami, G., Chatterjee, S., & Zhang, A. (2000) · The VLDB Journal · doi:10.1007/s007780050009 · Cited by 296 · see citing works
Hornik, K. (2005) · Journal of Statistical Software · doi:10.18637/jss.v014.i12 · Cited by 268 · see citing works
Measuring the Power of Hierarchical Cluster Analysis
Baker, F. B., & Hubert, L. J. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10480256 · Cited by 253 · see citing works
Sur la liaison et la division des points d'un ensemble fini
Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., & Zubrzycki, S. (1951) · Colloquium Mathematicum · doi:10.4064/cm-2-3-4-282-285 · Cited by 247 · see citing works
Practical Problems in a Method of Cluster Analysis
Marriott, F. H. C. (1971) · Biometrics · doi:10.2307/2528592 · Cited by 222 · see citing works
Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data
McQuitty, L. L. (1966) · Educational and Psychological Measurement · doi:10.1177/001316446602600402 · Cited by 206 · see citing works
Methods of Comparing Classifications
Rohlf, F. J. (1974) · Annual Review of Ecology and Systematics · doi:10.1146/annurev.es.05.110174.000533 · Cited by 165 · see citing works
Cluster Analysis Extended Rousseeuw et al
Maechler, M., Rousseeuw, P. J., Struyf, A., & Mia Hubert, B. (2014) · Cited by 103 · see citing works
A Criterion and an Algorithm for Grouping Data
Fukunaga, K., & Koontz, W. L. G. (1970) · IEEE Transactions on Computers · doi:10.1109/t-c.1970.222799 · Cited by 73 · see citing works
Frey, T., & van Groenewoud, H. (1972) · Journal of Ecology · doi:10.2307/2258571 · Cited by 45 · see citing works
A Stopping Rule for Partitioning Dendrograms
Hill, R. S. (1980) · Botanical Gazette · doi:10.1086/337163 · Cited by 35 · see citing works
Marozzi, M. (2012) · Quality & Quantity · doi:10.1007/s11135-012-9815-z · Cited by 28 · see citing works
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Longitudinal analyses reveal immunological misfiring in severe COVID-19
Lucas, C., Wong, P., Klein, J., Castro, T. B. R., Silva, J., Sundaram, M., Ellingson, M. K., Mao, T., Oh, J. E., Israelow, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2588-y · Cited by 2,074 · see citing works
Michel, C. M., & Koenig, T. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.062 · Cited by 1,370 · see citing works
Specification of tissue-resident macrophages during organogenesis
Mass, E., Ballesteros, I., Farlik, M., Halbritter, F., Günther, P., Crozet, L., Jacome-Galarza, C. E., Händler, K., Klughammer, J., Kobayashi, Y., et al. (2016) · Science · doi:10.1126/science.aaf4238 · Cited by 891 · see citing works
Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
Suzuki, K., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K. M., Mandla, R., Huerta-Chagoya, A., Melloni, G. E. M., Kanoni, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07019-6 · Cited by 606 · see citing works
Six modes of co-production for sustainability
Chambers, J. M., Wyborn, C. A., Ryan, M., Reid, R. S., Riechers, M., Şerban, A., Bennett, N. J., Cvitanovic, C., Fernández‐Giménez, M. E., Galvin, K. A., et al. (2021) · Nature Sustainability · doi:10.1038/s41893-021-00755-x · Cited by 535 · see citing works
Electroencephalographic Resting-State Networks: Source Localization of Microstates
Custo, A., Ville, D. V. D., Wells, W. M., Tomescu, M. I., Brunet, D., & Michel, C. M. (2017) · Brain Connectivity · doi:10.1089/brain.2016.0476 · Cited by 547 · see citing works
Applications of machine learning in animal behaviour studies
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505 · see citing works
More Than Smell—COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis
Parma, V., Ohla, K., Veldhuizen, M. G., Niv, M. Y., Kelly, C. E., Bakke, A. J., Cooper, K. W., Bouysset, C., Pirastu, N., Dibattista, M., et al. (2020) · Chemical Senses · doi:10.1093/chemse/bjaa041 · Cited by 503 · see citing works
Differences in gut microbiota profile between women with active lifestyle and sedentary women
Bressa, C., Bailén-Andrino, M., Pérez-Santiago, J., González-Soltero, R., Pérez, M., Montalvo-Lominchar, M. G., Maté-Muñoz, J. L., Domínguez, R., Moreno, D., & Larrosa, M. (2017) · PLOS ONE · doi:10.1371/journal.pone.0171352 · Cited by 501 · see citing works
Steenis, N. D., van Herpen, E., van der Lans, I. A., Ligthart, T. N., & van Trijp, H. C. M. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.06.036 · Cited by 493 · see citing works
Gong, Y., Ji, P., Yang, Y., Xie, S., Yu, T., Xiao, Y., Jin, M., Ma, D., Guo, L., Pei, Y., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.10.012 · Cited by 472 · see citing works
Copy number signatures and mutational processes in ovarian carcinoma
Macintyre, G., Goranova, T., Silva, D. D., Ennis, D. P., Piskorz, A., Eldridge, M., Sie, D., Lewsley, L.-A., Hanif, A., Wilson, C. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0179-8 · Cited by 434 · see citing works
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Ovaskainen, O., & Abrego, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108591720 · Cited by 404 · see citing works
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Lu, Z., Marand, A. P., Ricci, W. A., Ethridge, C. L., Zhang, X., & Schmitz, R. J. (2019) · Nature Plants · doi:10.1038/s41477-019-0548-z · Cited by 373 · see citing works
Moore, L. A. S., Niemi, J., Helmers, M. J., Liebman, M., Arbuckle, J. G., James, D. E., Kolka, R. K., O’Neal, M. E., Tomer, M. D., Tyndall, J., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1620229114 · Cited by 377 · see citing works
From cohorts to molecules: adverse impacts of endocrine disrupting mixtures
Caporale, N., Leemans, M., Birgersson, L., Germain, P.-L., Cheroni, C., Borbély, G., Engdahl, E., Lindh, C., Bressan, R. B., Cavallo, F., et al. (2022) · Science · doi:10.1126/science.abe8244 · Cited by 368 · see citing works
Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121598 · Cited by 335 · see citing works
Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI
Bréchet, L., Brunet, D., Birot, G., Rolf Gruetter, A., Michel, C. M., & Jorge, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.029 · Cited by 333 · see citing works
Schmid, M., Brianza, E., & Petko, D. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106586 · Cited by 394 · see citing works
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Guerin, E., Shkoporov, A. N., Stockdale, S. R., Clooney, A. G., Ryan, F. J., Sutton, T., Draper, L. A., González-Tortuero, E., Ross, R. P., & Hill, C. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.10.002 · Cited by 297 · see citing works
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Mottl, O., Flantua, S. G. A., Bhatta, K. P., Felde, V. A., Giesecke, T., Goring, S. J., Grimm, E. C., Haberle, S. G., Hooghiemstra, H., Ivory, S., et al. (2021) · Science · doi:10.1126/science.abg1685 · Cited by 309 · see citing works
Bos, L. D. J., Schouten, L. R. A., van Vught, L. A., Wiewel, M. A., Ong, D. S. Y., Cremer, O. L., Artigas, A., Martín‐Loeches, I., Hoogendijk, A. J., van der Poll, T., et al. (2017) · Thorax · doi:10.1136/thoraxjnl-2016-209719 · Cited by 298 · see citing works
Single-neuron projectome of mouse prefrontal cortex
Gao, L., Liu, S., Gou, L., Hu, Y., Liu, Y., Deng, L., Ma, D., Wang, H., Yang, Q., Chen, Z., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01041-5 · Cited by 264 · see citing works
Amruthnath, N., & Gupta, T. (2018) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iea.2018.8387124 · Cited by 289 · see citing works
Distinct Microbial Signatures Associated With Different Breast Cancer Types
Banerjee, S., Tian, T., Wei, Z., Shih, N., Feldman, M. D., Peck, K. N., DeMichele, A. M., Alwine, J. C., & Robertson, E. S. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00951 · Cited by 281 · see citing works
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Lyu, G., Bertolini, L., & Pfeffer, K. (2016) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2016.07.002 · Cited by 274 · see citing works
Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation
Asner, G. P., Martin, R. E., Knapp, D., Tupayachi, R., Anderson, C. B., Sinca, F., Vaughn, N. R., & Llactayo, W. (2017) · Science · doi:10.1126/science.aaj1987 · Cited by 266 · see citing works
Multidimensional endotypes of chronic rhinosinusitis and their association with treatment outcomes
Liao, B., Liu, J. ‐., Li, Z. ‐., Zhen, Z., Cao, P. ‐., Yao, Y., Long, X., Wang, H., Wang, Y., Schleimer, R. P., et al. (2018) · Allergy · doi:10.1111/all.13411 · Cited by 243 · see citing works
Bi, J., Wildani, A., Chang, H. H., & Liu, Y. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.9b06046 · Cited by 229 · see citing works
Transport mode detection based on mobile phone network data: A systematic review
Huang, H., Cheng, Y., & Weibel, R. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.02.008 · Cited by 206 · see citing works
Banerjee, S., Tian, T., Wei, Z., Shih, N., Feldman, M. D., Alwine, J. C., Coukos, G., & Robertson, E. S. (2017) · Oncotarget · doi:10.18632/oncotarget.16717 · Cited by 204 · see citing works
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IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge
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Mast cell activation test in the diagnosis of allergic disease and anaphylaxis
Bahri, R., Čustović, A., Korošec, P., Tsoumani, M., Barron, M. J., Wu, J., Sayers, R. L., Weimann, A., Ruiz-García, M., Patel, N., et al. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2018.01.043 · Cited by 195 · see citing works
Gou, M., Li, L., Ouyang, S., Wang, N., La, L., Liu, C., & Xiao, W. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127208 · Cited by 188 · see citing works
Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes
Zhang, H., Zang, C., Xu, Z., Zhang, Y., Xu, J., Bian, J., Morozyuk, D., Khullar, D., Zhang, Y., Nordvig, A. S., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02116-3 · Cited by 181 · see citing works
Functional specialization within the inferior parietal lobes across cognitive domains
Numssen, O., Bzdok, D., & Hartwigsen, G. (2021) · eLife · doi:10.7554/elife.63591 · Cited by 186 · see citing works
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Landler, L., Ruxton, G. D., & Malkemper, E. P. (2018) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-018-2538-y · Cited by 183 · see citing works
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Thompson, K. J., Ingle, J. N., Tang, X., Chia, N., Jeraldo, P. R., Walther-Antonio, M. R., Kandimalla, K. K., Johnson, S., Yao, J. Z., Harrington, S. C., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0188873 · Cited by 183 · see citing works
Varieties of abstract concepts and their multiple dimensions
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Yoon, M., Lee, J., & Jo, I. (2021) · The Internet and Higher Education · doi:10.1016/j.iheduc.2021.100806 · Cited by 188 · see citing works
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Liu, Y., Lü, Y., Fu, B., Harris, P., & Wu, L. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.09.082 · Cited by 163 · see citing works
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Ernande, L., Audureau, É., Jellis, C. L., Bergerot, C., Hénégar, C., Sawaki, D., Czibik, G., Volpi, C., Canouï‐Poitrine, F., Thibault, H., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.07.792 · Cited by 158 · see citing works
Murphy, M., Whitton, A. E., Deccy, S., Ironside, M. L., Rutherford, A. V., Beltzer, M., Sacchet, M. D., & Pizzagalli, D. A. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0749-1 · Cited by 153 · see citing works
Alkhayrat, M., Aljnidi, M., & Aljoumaa, K. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-0286-0 · Cited by 150 · see citing works
Pagani, M., Barsotti, N., Bertero, A., Trakoshis, S., Ulysse, L., Locarno, A., Misevičiūtė, I., Felice, A. D., Canella, C., Supekar, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26131-z · Cited by 148 · see citing works
Woolley, R. J., Ceelen, D. C. H., Ouwerkerk, W., Tromp, J., Figarska, S. M., Anker, S. D., Dickstein, K., Filippatos, G. S., Zannad, F. M., Metra, M., et al. (2021) · European Journal of Heart Failure · doi:10.1002/ejhf.2144 · Cited by 146 · see citing works
Fan, F., Liao, X., Lei, T., Zhao, T., Xia, M., Men, W., Wang, Y., Hu, M., Liu, J., Qin, S., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117581 · Cited by 144 · see citing works
Wybouw, N., Zhurov, V. V., Martel, C., Bruinsma, K. A., Hendrickx, F., Grbić, V., & Leeuwen, T. V. (2015) · Molecular Ecology · doi:10.1111/mec.13330 · Cited by 146 · see citing works
Pereira, M. M., & Frazzon, E. M. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102165 · Cited by 143 · see citing works
Plasmids Shaped the Recent Emergence of the Major Nosocomial Pathogen Enterococcus faecium
Arredondo-Alonso, S., Top, J., McNally, A., Puranen, S., Pesonen, M., Pensar, J., Marttinen, P., Braat, J. C., Rogers, M. R. C., van Schaik, W., et al. (2020) · mBio · doi:10.1128/mbio.03284-19 · Cited by 139 · see citing works
Song, C., Liu, B., Dai, Q., Li, H., & Mao, H. (2019) · Atmospheric Environment · doi:10.1016/j.atmosenv.2019.03.030 · Cited by 140 · see citing works
Cluster analysis and prediction of residential peak demand profiles using occupant activity data
Satre-Meloy, A., Diakonova, M., & Grünewald, P. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114246 · Cited by 140 · see citing works
Lessons Learned from a Two-Round Delphi-based Scenario Study
Schmalz, U., Spinler, S., & Ringbeck, J. (2020) · MethodsX · doi:10.1016/j.mex.2020.101179 · Cited by 138 · see citing works
Tomescu, M. I., Rihs, T. A., Rochas, V., Hardmeier, M., Britz, J., Allali, G., Fuhr, P., D, S. E. M., & Michel, C. M. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.04.011 · Cited by 153 · see citing works
Spatial patterns and broad‐scale weather cues of beech mast seeding in Europe
Vacchiano, G., Hacket‐Pain, A. J., Turco, M., Motta, R., Maringer, J., Conedera, M., Drobyshev, I., & Ascoli, D. (2017) · New Phytologist · doi:10.1111/nph.14600 · Cited by 135 · see citing works
Integrated multi-omics analysis of ovarian cancer using variational autoencoders
Hira, M. T., Razzaque, M. A., Angione, C., Scrivens, J., Sawan, S., & Sarker, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-85285-4 · Cited by 134 · see citing works
Kemsawasd, V., Viana, T., Ardö, Y., & Arneborg, N. (2015) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-015-6835-3 · Cited by 133 · see citing works
Rocha, B. A., Asimakopoulos, A. G., Honda, M., da Costa, N. L., Barbosa, R., Barbosa, F., & Kannan, K. (2018) · Environment International · doi:10.1016/j.envint.2018.04.023 · Cited by 133 · see citing works
Rapid defense responses in maize leaves induced by Spodoptera exigua caterpillar feeding
Tzin, V., Hojo, Y., Strickler, S. R., Bartsch, L. J., Archer, C. M., Ahern, K. R., Zhou, S., Christensen, S. A., Gális, I., Mueller, L. A., et al. (2017) · Journal of Experimental Botany · doi:10.1093/jxb/erx274 · Cited by 130 · see citing works
Pennlert, J., Overholser, R., Asplund, K., Carlberg, B., Rompaye, B. V., Wiklund, P.-G., & Eriksson, M. (2016) · Stroke · doi:10.1161/strokeaha.116.014643 · Cited by 129 · see citing works
Celestino, A. E. M., Cruz, D. A. M., Sánchez, O., Reyes, F. G., & Soto, D. V. (2018) · Water · doi:10.3390/w10040437 · Cited by 129 · see citing works
The Semantic Content of Abstract Concepts: A Property Listing Study of 296 Abstract Words
Harpaintner, M., Trumpp, N. M., & Kiefer, M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01748 · Cited by 129 · see citing works
Microbiome signatures in prostate cancer
Banerjee, S., Alwine, J. C., Wei, Z., Tian, T., Shih, N. N. C., Sperling, C. D., Guzzo, T. J., Feldman, M. D., & Robertson, E. S. (2019) · Carcinogenesis · doi:10.1093/carcin/bgz008 · Cited by 129 · see citing works
van Rentergem, J. A. A., Deserno, M. K., & Geurts, H. M. (2021) · Clinical Psychology Review · doi:10.1016/j.cpr.2021.102033 · Cited by 130 · see citing works
The social and cultural roots of whale and dolphin brains
Fox, K. C. R., Muthukrishna, M., & Shultz, S. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0336-y · Cited by 128 · see citing works
Gosal, A. S., Geijzendorffer, I. R., Václavík, T., Poulin, B., & Ziv, G. (2019) · Ecosystem Services · doi:10.1016/j.ecoser.2019.100958 · Cited by 128 · see citing works
Liu, F., Qin, L., Liao, Z., Song, J., Yuan, C., Liu, Y., Wang, Y., Xu, H., Zhang, Q., Pei, Y., et al. (2020) · Experimental Hematology and Oncology · doi:10.1186/s40164-020-00165-3 · Cited by 128 · see citing works
Frontera, J. L., Aïssa, H. B., Sala, R. W., Mailhes‐Hamon, C., Georgescu, I. A., Léna, C., & Popa, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18953-0 · Cited by 126 · see citing works
Patterns of Arterial Disease in Takayasu Arteritis and Giant Cell Arteritis
Gribbons, K. B., Ponte, C., Carette, S., Craven, A., Cuthbertson, D. D., Hoffman, G. S., KHALIDI, N. A., Koening, C. L., Langford, C. A., Maksimowicz‐McKinnon, K., et al. (2019) · Arthritis Care & Research · doi:10.1002/acr.24055 · Cited by 124 · see citing works
Hançer, E., & Karaboğa, D. (2016) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2016.06.004 · Cited by 120 · see citing works
Pang, Y., Ali, J., Wang, X., Franje, N. J., Revilleza, J. E., Xu, J., & Li, Z. (2016) · PLoS ONE · doi:10.1371/journal.pone.0168483 · Cited by 120 · see citing works
Nixon, N., Nixon, T. M., & Graesser, A. C. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1102-z · Cited by 120 · see citing works
Camiolo, M. J., Gauthier, M. C., Kaminski, N., Ray, A., & Wenzel, S. E. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.05.051 · Cited by 120 · see citing works
Gaudette, B. T., Jones, D. D., Bortnick, A., Argon, Y., & Allman, D. (2020) · Nature Communications · doi:10.1038/s41467-019-14032-1 · Cited by 117 · see citing works
Sensory Discrimination of Blood and Floral Nectar by Aedes aegypti Mosquitoes
Jové, V., Gong, Z., Hol, F. J. H., Zhao, Z., Sorrells, T. R., Carroll, T. S., Prakash, M., McBride, C. S., & Vosshall, L. B. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.019 · Cited by 109 · see citing works
SPIN reveals genome-wide landscape of nuclear compartmentalization
Wang, Y., Zhang, Y., Zhang, R., van Schaik, T., Zhang, L., Sasaki, T., Peric‐Hupkes, D., Chen, Y., Gilbert, D. M., van Steensel, B., et al. (2021) · Genome biology · doi:10.1186/s13059-020-02253-3 · Cited by 114 · see citing works
Gschwind, M., Hardmeier, M., Ville, D. V. D., Tomescu, M. I., Penner, I., Naegelin, Y., Fuhr, P., Michel, C. M., & Seeck, M. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.08.008 · Cited by 114 · see citing works
The skull roof tracks the brain during the evolution and development of reptiles including birds
Fabbri, M., Mongiardino Koch, N., Pritchard, A. C., Hanson, M., Hoffman, E., Bever, G. S., Balanoff, A. M., Morris, Z. S., Field, D. J., Camacho, J., et al. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0288-2 · Cited by 112 · see citing works
Predicting loan default in peer‐to‐peer lending using narrative data
Xia, Y., He, L., Li, Y., Liu, N., & Ding, Y. (2019) · Journal of Forecasting · doi:10.1002/for.2625 · Cited by 110 · see citing works
Gut virome profiling identifies a widespread bacteriophage family associated with metabolic syndrome
de Jonge, P. A., Wortelboer, K., Scheithauer, T. P. M., van den Born, B. H., Zwinderman, A. H., Nóbrega, F. L., Dutilh, B. E., Nieuwdorp, M., & Herrema, H. (2022) · Nature Communications · doi:10.1038/s41467-022-31390-5 · Cited by 106 · see citing works
The evolution, complexity and diversity of models of long‐term forest dynamics
Bugmann, H. K. M., & Seidl, R. (2022) · Journal of Ecology · doi:10.1111/1365-2745.13989 · Cited by 105 · see citing works
The Influence of Personality, Resilience, and Alexithymia on Mental Health During COVID-19 Pandemic
Osimo, S. A., Aiello, M., Gentili, C., Ionta, S., & Cecchetto, C. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.630751 · Cited by 106 · see citing works
Shukla, R., Agarwal, A., Sachdeva, K., Kurths, J., & Joshi, P. K. (2018) · Climatic Change · doi:10.1007/s10584-018-2314-z · Cited by 106 · see citing works
Christenson, S. A., van den Berge, M., Faiz, A., Inkamp, K., Bhakta, N. R., Bonser, L. R., Zlock, L. T., Barjaktarević, I., Barr, R. G., Bleecker, E. R., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci121087 · Cited by 106 · see citing works
Choi, S., Hoffman, E. A., Wenzel, S. E., Castro, M., Fain, S. B., Jarjour, N. N., Schiebler, M. L., Chen, K., & Lin, C. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2016.11.053 · Cited by 105 · see citing works
Patterns of Social Determinants of Health and Child Mental Health, Cognition, and Physical Health
Xiao, Y., Mann, J. J., Chow, J. C., Brown, T. T., Snowden, L. R., Yip, P. S.-F., Tsai, A. C., Hou, Y., Pathak, J., Wang, F., et al. (2023) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2023.4218 · Cited by 108 · see citing works
Cohn, R., & Holm, E. A. (2021) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-021-00205-8 · Cited by 104 · see citing works
Developing urban residential reference buildings using clustering analysis of satellite images
Li, X., Yao, R., Liu, M., Costanzo, V., Yu, W., Wang, W., Short, C. A., & Li, B. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.03.064 · Cited by 102 · see citing works
Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes
Savoy, P. R., Appling, A., Heffernan, J. B., Stets, E. G., Read, J. S., Harvey, J. W., & Bernhardt, E. S. (2019) · Limnology and Oceanography · doi:10.1002/lno.11154 · Cited by 104 · see citing works
Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts
Zelezniak, A., Vowinckel, J., Capuano, F., Messner, C. B., Demichev, V., Polowsky, N., Mülleder, M., Kamrad, S., Klaus, B., Keller, M. A., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.08.001 · Cited by 100 · see citing works
- 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)
- 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 · 2013: 1
- 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 › Computer Science › Artificial Intelligence › 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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