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Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP journal on wireless communications and networking, 2021, 1-16.ShiC.WeiB.WeiS.WangW.LiuH. & LiuJ. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP journal on wireless communications and networking, 2021, 1-16.Search in Google Scholar
Bai, L., Cheng, X., Liang, J., Shen, H., & Guo, Y. (2017). Fast density clustering strategies based on the k-means algorithm. Pattern Recognition, 71, 375-386.BaiL.ChengX.LiangJ.ShenH. & GuoY. (2017). Fast density clustering strategies based on the k-means algorithm. Pattern Recognition, 71, 375-386.Search in Google Scholar
Ahmad, A., & Khan, S. S. (2019). Survey of state-of-the-art mixed data clustering algorithms. Ieee Access, 7, 31883-31902.AhmadA. & KhanS. S. (2019). Survey of state-of-the-art mixed data clustering algorithms. Ieee Access, 7, 31883-31902.Search in Google Scholar
Chen, X., Fain, B., Lyu, L., & Munagala, K. (2019, May). Proportionally fair clustering. In International conference on machine learning (pp. 1032-1041). PMLR.ChenX.FainB.LyuL. & MunagalaK. (2019, May). Proportionally fair clustering. In International conference on machine learning (pp. 1032-1041). PMLR.Search in Google Scholar
Rao, P. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless networks, 23, 2005-2020.RaoP. S.JanaP. K. & BankaH. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless networks, 23, 2005-2020.Search in Google Scholar
Wierzchoń, S. T., & Kłopotek, M. A. (2018). Modern algorithms of cluster analysis (Vol. 34). Springer International Publishing.WierzchońS. T. & KłopotekM. A. (2018). Modern algorithms of cluster analysis (Vol. 34). Springer International Publishing.Search in Google Scholar
Cohen-Addad, V., Kanade, V., Mallmann-Trenn, F., & Mathieu, C. (2019). Hierarchical clustering: Objective functions and algorithms. Journal of the ACM (JACM), 66(4), 1-42.Cohen-AddadV.KanadeV.Mallmann-TrennF. & MathieuC. (2019). Hierarchical clustering: Objective functions and algorithms. Journal of the ACM (JACM), 66(4), 1-42.Search in Google Scholar
Wang, J., Cao, Y., Li, B., Kim, H. J., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems, 76, 452-457.WangJ.CaoY.LiB.KimH. J. & LeeS. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems, 76, 452-457.Search in Google Scholar
Tang, C., Zhu, X., Liu, X., Li, M., Wang, P., Zhang, C., & Wang, L. (2018). Learning a joint affinity graph for multiview subspace clustering. IEEE Transactions on Multimedia, 21(7), 1724-1736.TangC.ZhuX.LiuX.LiM.WangP.ZhangC. & WangL. (2018). Learning a joint affinity graph for multiview subspace clustering. IEEE Transactions on Multimedia, 21(7), 1724-1736.Search in Google Scholar
Huang, D., Wang, C. D., Wu, J. S., Lai, J. H., & Kwoh, C. K. (2019). Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1212-1226.HuangD.WangC. D.WuJ. S.LaiJ. H. & KwohC. K. (2019). Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1212-1226.Search in Google Scholar
Bryant, A., & Cios, K. (2017). RNN-DBSCAN: A density-based clustering algorithm using reverse nearest neighbor density estimates. IEEE Transactions on Knowledge and Data Engineering, 30(6), 1109-1121.BryantA. & CiosK. (2017). RNN-DBSCAN: A density-based clustering algorithm using reverse nearest neighbor density estimates. IEEE Transactions on Knowledge and Data Engineering, 30(6), 1109-1121.Search in Google Scholar
Yang, B., Fu, X., Sidiropoulos, N. D., & Hong, M. (2017, July). Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In international conference on machine learning (pp. 3861-3870). PMLR.YangB.FuX.SidiropoulosN. D. & HongM. (2017, July). Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In international conference on machine learning (pp. 3861-3870). PMLR.Search in Google Scholar
Yuan, G., Sun, P., Zhao, J., Li, D., & Wang, C. (2017). A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 47, 123-144.YuanG.SunP.ZhaoJ.LiD. & WangC. (2017). A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 47, 123-144.Search in Google Scholar
Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., ... & Lin, C. T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681.SaxenaA.PrasadM.GuptaA.BharillN.PatelO. P.TiwariA. ... & LinC. T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681.Search in Google Scholar
Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.EzugwuA. E.IkotunA. M.OyeladeO. O.AbualigahL.AgushakaJ. O.EkeC. I. & AkinyeluA. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.Search in Google Scholar
Lahari, K., Murty, M. R., & Satapathy, S. C. (2015). Partition based clustering using genetic algorithm and teaching learning based optimization: performance analysis. In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2 (pp. 191-200). Springer International Publishing.LahariK.MurtyM. R. & SatapathyS. C. (2015). Partition based clustering using genetic algorithm and teaching learning based optimization: performance analysis. In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2 (pp. 191-200). Springer International Publishing.Search in Google Scholar
Campello, R. J., Kröger, P., Sander, J., & Zimek, A. (2020). Density‐based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1343.CampelloR. J.KrögerP.SanderJ. & ZimekA. (2020). Density‐based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1343.Search in Google Scholar
Cheng, W., Wang, W., & Batista, S. (2018). Grid-based clustering. In Data clustering (pp. 128-148). Chapman and Hall/CRC.ChengW.WangW. & BatistaS. (2018). Grid-based clustering. In Data clustering (pp. 128-148). Chapman and Hall/CRC.Search in Google Scholar
Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review, 56(8), 8219-8264.RanX.XiY.LuY.WangX. & LuZ. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review, 56(8), 8219-8264.Search in Google Scholar
Wang, H., Yang, Y., Liu, B., & Fujita, H. (2019). A study of graph-based system for multi-view clustering. Knowledge-Based Systems, 163, 1009-1019.WangH.YangY.LiuB. & FujitaH. (2019). A study of graph-based system for multi-view clustering. Knowledge-Based Systems, 163, 1009-1019.Search in Google Scholar
Sardar, T. H., & Ansari, Z. (2018). Partition based clustering of large datasets using MapReduce framework: An analysis of recent themes and directions. Future Computing and Informatics Journal, 3(2), 247-261.SardarT. H. & AnsariZ. (2018). Partition based clustering of large datasets using MapReduce framework: An analysis of recent themes and directions. Future Computing and Informatics Journal, 3(2), 247-261.Search in Google Scholar
Wang, J., Zhu, C., Zhou, Y., Zhu, X., Wang, Y., & Zhang, W. (2017). From partition-based clustering to density-based clustering: Fast find clusters with diverse shapes and densities in spatial databases. IEEE access, 6, 1718-1729.WangJ.ZhuC.ZhouY.ZhuX.WangY. & ZhangW. (2017). From partition-based clustering to density-based clustering: Fast find clusters with diverse shapes and densities in spatial databases. IEEE access, 6, 1718-1729.Search in Google Scholar
Du, X., He, Y., & Huang, J. Z. (2021, December). Random sample partition-based clustering ensemble algorithm for big data. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5885-5887). IEEE.DuX.HeY. & HuangJ. Z. (2021, December). Random sample partition-based clustering ensemble algorithm for big data. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5885-5887). IEEE.Search in Google Scholar
Bhattacharjee, P., & Mitra, P. (2021). A survey of density based clustering algorithms. Frontiers of Computer Science, 15, 1-27.BhattacharjeeP. & MitraP. (2021). A survey of density based clustering algorithms. Frontiers of Computer Science, 15, 1-27.Search in Google Scholar
Starczewski, A., Scherer, M. M., Książek, W., Dębski, M., & Wang, L. (2021). A novel grid-based clustering algorithm. Journal of Artificial Intelligence and Soft Computing Research, 11(4), 319-330.StarczewskiA.SchererM. M.KsiążekW.DębskiM. & WangL. (2021). A novel grid-based clustering algorithm. Journal of Artificial Intelligence and Soft Computing Research, 11(4), 319-330.Search in Google Scholar
dos Santos, J. A., Syed, T. I., Naldi, M. C., Campello, R. J., & Sander, J. (2019). Hierarchical density-based clustering using MapReduce. IEEE Transactions on Big Data, 7(1), 102-114.dos SantosJ. A.SyedT. I.NaldiM. C.CampelloR. J. & SanderJ. (2019). Hierarchical density-based clustering using MapReduce. IEEE Transactions on Big Data, 7(1), 102-114.Search in Google Scholar
Pandove, D., Rani, R., & Goel, S. (2017). Local graph based correlation clustering. Knowledge-Based Systems, 138, 155-175.PandoveD.RaniR. & GoelS. (2017). Local graph based correlation clustering. Knowledge-Based Systems, 138, 155-175.Search in Google Scholar
Wei Fan,Toyohide Watanabe & Koichi Asakura. (2010). Mining underlying correlated-clusters in high-dimensional data streams. Int. J. of Social and Humanistic Computing(3),282-299.FanWeiWatanabeToyohide & AsakuraKoichi. (2010). Mining underlying correlated-clusters in high-dimensional data streams. Int. J. of Social and Humanistic Computing(3),282-299.Search in Google Scholar
(2020). Networks - Mobile Networks; Research Data from Nanjing University of Posts and Telecommunications Update Understanding of Mobile Networks (Performance Analysis of an Energy-efficient Clustering Algorithm for Coordination Networks). Network Weekly News.(2020). Networks - Mobile Networks; Research Data from Nanjing University of Posts and Telecommunications Update Understanding of Mobile Networks (Performance Analysis of an Energy-efficient Clustering Algorithm for Coordination Networks). Network Weekly News.Search in Google Scholar
Josemila Baby Jesuretnam & Jeba James Rose. (2020). Performance analysis of optimal cluster selection and intrusion detection by hierarchical K-means clustering with hybrid ABC-DT. International Journal of Pervasive Computing and Communications(1),49-63.JesuretnamJosemila Baby & RoseJeba James. (2020). Performance analysis of optimal cluster selection and intrusion detection by hierarchical K-means clustering with hybrid ABC-DT. International Journal of Pervasive Computing and Communications(1),49-63.Search in Google Scholar