MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation

References

• M. Ahuja and J. Bal, Exploring cluster analysis, Int. J. Comput. Inf. Technol 3 (2014), pp. 594–597.
   Google Scholar
• A. Asuncion and D. Newman, Uci machine learning repository (2007).
   Google Scholar
• A. Azzalini and G. Menardi, Clustering via nonparametric density estimation: The r package pdfcluster, arXiv preprint arXiv:1301.6559 (2013).
   Google Scholar
• A. Azzalini and N. Torelli, Clustering via nonparametric density estimation, Stat. Comput. 17 (2007), pp. 71–80. doi: https://doi.org/10.1007/s11222-006-9010-y
   Web of Science ®Google Scholar
• J.E. Chacon and T. Duong, Multivariate Kernel Smoothing and Its Applications, Chapman and Hall/CRC, Boca Raton, FL, 2018.
   Google Scholar
• M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Łukasik, and S. Żak, Complete gradient clustering algorithm for features analysis of x-ray images, in Information Technologies in Biomedicine, Vol. 69, Springer, 2010, pp. 15–24. doi: https://doi.org/10.1007/978-3-642-13105-9_2
   Google Scholar
• J. Chen, K. Li, H. Rong, K. Bilal, N. Yang, and K. Li, A disease diagnosis and treatment recommendation system based on big data mining and cloud computing, Inf. Sci. (Ny) 435 (2018), pp. 124–149. doi: https://doi.org/10.1016/j.ins.2018.01.001
   Web of Science ®Google Scholar
• Y. Cheng, Mean shift, mode seeking, and clustering, IEEE. Trans. Pattern. Anal. Mach. Intell. 17 (1995), pp. 790–799. doi: https://doi.org/10.1109/34.400568
   Web of Science ®Google Scholar
• A. Cichocki, R. Zdunek, A.H. Phan, and S.i. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to ExploratoryMulti-way Data Analysis and Blind Source Separation, John Wiley & Sons, Chichester, WSX, 2009.
   Google Scholar
• D. Defays, An efficient algorithm for a complete-link method, Comput. J. 20 (1977), pp. 364–366. doi: https://doi.org/10.1093/comjnl/20.4.364
   Web of Science ®Google Scholar
• M. Duan, K. Li, X. Liao, K. Li, and Q. Tian, Features-enhanced multi-attribute estimation with convolutional tensor correlation fusion network, ACM Trans. Multimed. Comput. Commun. Appl. 15 (2019), pp. 1–23.
   Web of Science ®Google Scholar
• M. Duan, K. Li, and Q. Tian, A novel multi-task tensor correlation neural network for facial attribute prediction, arXiv preprint arXiv:1804.02810 (2018).
   Google Scholar
• M. Ester, H.P. Kriegel, J. Sander, X. Xu et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in Kdd 96 (1996), pp. 226–231.
   Google Scholar
• M. Forina, C. Armanino, S. Lanteri, and E. Tiscornia, Classification of olive oils from their fatty acid composition, in Food Research and Data Analysis: proceedings from the IUFoST Symposium, September 20–23, 1982, Oslo, Norway, H. Martens and H. Russwurm Jr., eds., Applied Science Publishers, London, 1983.
   Google Scholar
• A.E. Fraley C.and Raftery, T.B. Murphy, and L. Scrucca, mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation, in Technical Report No. 597, Department of Statistics, University of Washington. 2012.
   Google Scholar
• C. Fraley and A.E. Rafter, Bayesian regularization for normal mixture estimation and model-based clustering, J. Classif. 24 (2007), pp. 155–181. doi: https://doi.org/10.1007/s00357-007-0004-5
   Web of Science ®Google Scholar
• C. Fraley and A.E. Raftery, Model-based clustering, discriminant analysis and density estimation, J. Am. Stat. Assoc. 97 (2002), pp. 611–631. doi: https://doi.org/10.1198/016214502760047131
   Web of Science ®Google Scholar
• J. Gan and Y. Tao, Dynamic density based clustering, in Proceedings of the 2017 ACM International Conference on Management of Data, New York, NY, USA, Association for Computing Machinery, 2017, pp. 1493–1507.
   Google Scholar
• A. Gramacki, Nonparametric Kernel Density Estimation and Its Computational Aspects, Springer, Cham, CHE, 2018.
   Google Scholar
• T. Gupta and S.P. Panda, A comparison of k-means clustering algorithm and clara clustering algorithm on iris dataset, Int. J. Eng. Technol. 7 (2018), pp. 4766–4768.
   Google Scholar
• J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques Third Edition, Morgan Kaufmann, Burlington, MA, 2011.
   Google Scholar
• H. Huang, C. Ding, D. Luo, and T. Li, Simultaneous tensor subspace selection and clustering: the equivalence of high order svd and k-means clustering, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, 2008, pp. 327–335.
   Google Scholar
• A. Kassambara, Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning, Vol. 1, STHDA, 2017. Available at http://www.sthda.com/english/download/3-ebooks/.
   Google Scholar
• P.J. Kaufman and L. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Vol. 344, Wiley-Interscience, Hoboken, NJ, 2009.
   Google Scholar
• P. Kulczycki and M. Charytanowicz, A complete gradient clustering algorithm formed with kernel estimators, Int. J. Appl. Math. Comput. Sci. 20 (2010), pp. 123–134. doi: https://doi.org/10.2478/v10006-010-0009-3
   Web of Science ®Google Scholar
• L. Liao, K. Li, K. Li, Q. Tian, and C. Yang, Automatic density clustering with multiple kernels for high-dimension bioinformatics data, in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2017, pp. 2105–2112.
   Google Scholar
• Y. Luo, D. Tao, K. Ramamohanarao, C. Xu, and Y. Wen, Tensor canonical correlation analysis for multi-view dimension reduction,  Transactions on Knowledge and Data Engineering IEEE. Trans. Knowl. Data. Eng. 27 (2015), pp. 3111–3124. doi: https://doi.org/10.1109/TKDE.2015.2445757
   Web of Science ®Google Scholar
• J. MacQueen et al., Some methods for classification and analysis of multivariate observations, in Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1. Oakland, CA, USA, 1967, pp. 281–297.
   Google Scholar
• R. Maitra and V. Melnyko, Simulating data to study performance of nite mixture modeling and clustering algorithms, J. Comput. Graph. Stat. 19 (2010), pp. 354–376. doi: https://doi.org/10.1198/jcgs.2009.08054
   Web of Science ®Google Scholar
• L. Matioli, S. Santos, M. Kleina, and E. Leite, A new algorithm for clustering based on kernel density estimation, J. Appl. Stat. 45 (2018), pp. 347–366. doi: https://doi.org/10.1080/02664763.2016.1277191
   Web of Science ®Google Scholar
• G.J. McLachlan and T. Krishnan, The EM Algorithm and Extensions, Vol. 382, Wiley Series in Probability and Mathematical Statistics – John Wiley & Sons, New York, 2007.
   Google Scholar
• G. Menardi and A. Azzalini, An advancement in clustering via nonparametric density estimation, Stat. Comput. 24 (2014), pp. 753–767. doi: https://doi.org/10.1007/s11222-013-9400-x
   Web of Science ®Google Scholar
• J. Nocedal and S. Wright, Numerical Optimization. Springer, New York, 1999.
   Google Scholar
• R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2018. Available at https://www.R-project.org/.
   Google Scholar
• S.L. Race, Iterative Consensus Clustering, North Carolina State University, Raleigh, NC, 2014.
   Google Scholar
• A. Rodrìguez and A. Laio, Clustering by fast search and find of density peaks, Science 344 (2014), pp. 1492–1496. doi: https://doi.org/10.1126/science.1242072
   PubMed Web of Science ®Google Scholar
• P.J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math. 20 (1987), pp. 53–65. doi: https://doi.org/10.1016/0377-0427(87)90125-7
   Web of Science ®Google Scholar
• D.W. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization, John Wiley & Sons, Hoboken, NJ, 2015.
   Google Scholar
• L. Scrucca, M. Fop, T.B. Murphy, and A.E. Raftery, mclust 5: Clustering, classification and density estimation using gaussian finite mixture models, R. J. 8 (2016), pp. 205–233. doi: https://doi.org/10.32614/RJ-2016-021
   Google Scholar
• G.H. Shah, C. Bhensdadia, and A.P. Ganatra, An empirical evaluation of density-based clustering techniques, Int. J. Soft Comput. Eng. 22312307 (2012), pp. 216–223.
   Google Scholar
• R. Sibson, Slink: An optimally efficient algorithm for the single-link cluster method, Comput. J. 16 (1973), pp. 30–34. doi: https://doi.org/10.1093/comjnl/16.1.30
   Web of Science ®Google Scholar
• B.W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman and Hall, London, 1986.
   Google Scholar
• W.W. Sun and L. Li, Dynamic tensor clustering, J. Am. Stat. Assoc. 114 (2019), pp. 1–28. doi: https://doi.org/10.1080/01621459.2019.1660170
   Web of Science ®Google Scholar
• J. Wu, Z. Lin, and H. Zha, Essential tensor learning for multi-view spectral clustering, IEEE. Trans. Image. Process. 28 (2019), pp. 5910–5922. doi: https://doi.org/10.1109/TIP.2019.2916740
   PubMed Web of Science ®Google Scholar
• J. Xie, H. Gao, W. Xie, X. Lui, and P.W. Grant, Robust clustering by detecting density peaks and assigning points based on fuzzy weighted k-nearest neighbors, Inf. Sci. (Ny) 7 (2016), pp. 10–35.
   Google Scholar