Initial Seeds Selection for K-means Clustering Based on Outlier Detection

Author:

Yang Zhiyong1,Jiang Feng1,Yu Xu1,Du Junwei1

Affiliation:

1. Qingdao University of Science and Technology, China

Funder

National Natural Science Foundation of China

e Natural Science Foundation of Shandong Province, China

Publisher

ACM

Reference23 articles.

1. Maurizio Filippone , Francesco Camastra , Francesco Masulli and Stefano Rovetta . 2008 . A survey of kernel and spectral methods for clustering. Pattern recognition 41, 1 (January 2008), 176-190. https://doi.org/10.1016/j.patcog.2007.05.018 10.1016/j.patcog.2007.05.018 Maurizio Filippone, Francesco Camastra, Francesco Masulli and Stefano Rovetta. 2008. A survey of kernel and spectral methods for clustering. Pattern recognition 41, 1 (January 2008), 176-190. https://doi.org/10.1016/j.patcog.2007.05.018

2. M. EmreCelebi , Hassan A. Kingravi and Patricio A . Vela . 2013 . A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert systems with applications 40, 1 (January 2013), 200-210. https://doi.org/10.1016/j.eswa.2012.07.021 10.1016/j.eswa.2012.07.021 M. EmreCelebi, Hassan A.Kingravi and Patricio A.Vela. 2013. A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert systems with applications 40, 1 (January 2013), 200-210. https://doi.org/10.1016/j.eswa.2012.07.021

3. J MacQueen . 1967 . Some methods for classification and analysis of multivariate observations . In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability . Berkeley, CA , 281 - 297 . J MacQueen. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability. Berkeley, CA, 281-297.

4. Initializing K-means clustering by bootstrap and data depth;Torrente Aurora;Journal of Classification,2020

5. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost

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