This work is licensed under the Creative Commons Attribution 4.0 International License.
V. Srivastava and B. Biswas, “An optimization based framework for region wise optimal clusters in MR images using hybrid objective,” Neurocomputing, vol. 541, Jul. 2023, Art. no. 126286. https://doi.org/10.1016/j.neucom.2023.126286Search in Google Scholar
M. Woźniak and D. Połap, “Object detection and recognition via clustered features,” Neurocomputing, vol. 320, pp. 76–84, Dec. 2018. https://doi.org/10.1016/j.neucom.2018.09.003Search in Google Scholar
N. Dong, B. Ren, H. Li, X. Zhong, X. Gong, J. Han, J. Lv, and J. Cheng, “A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests,” Information Sciences, vol. 637, Aug. 2023, Art. no. 118979. https://doi.org/10.1016/j.ins.2023.118979Search in Google Scholar
M. Nicholson, R. Agrahari, C. Conran, H. Assem, and J. D. Kelleher, “The interaction of normalisation and clustering in sub-domain definition for multi-source transfer learning based time series anomaly detection,” Knowledge-Based Systems, vol. 257, Dec. 2022, Art. no. 109894. https://doi.org/10.1016/j.knosys.2022.109894Search in Google Scholar
S. C. Basak, V. R. Magnuson, G. J. Niemi, and R. R. Regal, “Determining structural similarity of chemicals using graph-theoretic indices,” Discrete Applied Mathematics, vol. 19, no. 1–3, pp. 17–44, Mar. 1988. https://doi.org/10.1016/0166-218X(88)90004-2Search in Google Scholar
K. Schatz, F. Frieß, M. Schäfer, P. C. F. Buchholz, J. Pleiss, T. Ertl, and M. Krone, “Analyzing the similarity of protein domains by clustering Molecular Surface Maps,” Computers & Graphics, vol. 99, pp. 114–127, Oct. 2021. https://doi.org/10.1016/j.cag.2021.06.007Search in Google Scholar
K. Mohammadpour, A. Rashki, M. Sciortino, D. G. Kaskaoutis, and A. D. Boloorani, “A statistical approach for identification of dust-AOD hotspots climatology and clustering of dust regimes over Southwest Asia and the Arabian Sea,” Atmospheric Pollution Research, vol. 13, no. 4, Apr. 2022, Art. no. 101395. https://doi.org/10.1016/j.apr.2022.101395Search in Google Scholar
M. Balcilar, A. H. Elsayed, and S. Hammoudeh, “Financial connectedness and risk transmission among MENA countries: Evidence from connectedness network and clustering analysis,” Journal of International Financial Markets, Institutions and Money, vol. 82, Jan. 2023, Art. no. 101656. https://doi.org/10.1016/j.intfin.2022.101656Search in Google Scholar
A. M. Dichiarante, N. Langet, R. A. Bauer, B. P. Goertz-Allmann, S. C. Williams-Stroud, D. Kühn, V. Oye, S. E. Greenberg, and B. D. E. Dando, “Identifying geological structures through microseismic cluster and burst analyses complementing active seismic interpretation,” Tectonophysics, vol. 820, Dec. 2021, Art. no. 229107. https://doi.org/10.1016/j.tecto.2021.229107Search in Google Scholar
V. V. Romanuke, “Fast-and-smoother uplink power control algorithm based on distance ratios for wireless data transfer systems,” Studies in Informatics and Control, vol. 28, no. 2, pp. 147–156, 2019. https://doi.org/10.24846/v28i2y201903Search in Google Scholar
V. V. Romanuke, “An uplink power control routine for quality-of-service equalization in wireless data transfer networks constrained to equidistant power levels,” KPI Science News, no. 2, pp. 46–56, 2019. https://doi.org/10.20535/kpi-sn.2019.2.160199Search in Google Scholar
Z. Zhang, Q. Feng, J. Huang, and J. Wang, “Improved approximation algorithms for solving the squared metric k-facility location problem,” Theoretical Computer Science, vol. 942, pp. 107–122, Jan. 2023. https://doi.org/10.1016/j.tcs.2022.11.027Search in Google Scholar
S. Li, “A 1.488 approximation algorithm for the uncapacitated facility location problem,” in Automata, Languages and Programming. Lecture Notes in Computer Science, L. Aceto, M. Henzinger, and J. Sgall, Eds., vol. 6756. Springer, Berlin, Heidelberg, 2011, pp. 77–88. https://doi.org/10.1007/978-3-642-22012-8_5Search in Google Scholar
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Information Sciences, vol. 622, pp. 178–210, Apr. 2023. https://doi.org/10.1016/j.ins.2022.11.139Search in Google Scholar
M. E. Celebi, H. A. Kingravi, and P. A. Vela, “A comparative study of efficient initialization methods for the k-means clustering algorithm,” Expert Systems with Applications, vol. 40, no. 1, pp. 200–210, Jan. 2013. https://doi.org/10.1016/j.eswa.2012.07.021Search in Google Scholar
M. Mahajan, P. Nimbhorkar, and K. Varadarajan, “The planar k-means problem is NP-hard,” in WALCOM: Algorithms and Computation. Lecture Notes in Computer Science, S. Das and R. Uehara, Eds., vol. 5431. Springer, Berlin, Heidelberg, 2009, pp. 274–285. https://doi.org/10.1007/978-3-642-00202-1_24Search in Google Scholar
T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu, “A local search approximation algorithm for k-means clustering,” Computational Geometry: Theory and Applications, vol. 28, no. 2–3, pp. 89–112, Jun. 2004. https://doi.org/10.1016/j.comgeo.2004.03.003Search in Google Scholar
P. Fränti and S. Sieranoja, “How much can k-means be improved by using better initialization and repeats?” Pattern Recognition, vol. 93, pp. 95–112, Sep. 2019. https://doi.org/10.1016/j.patcog.2019.04.014Search in Google Scholar
V. V. Romanuke, “Optimization of a dataset for a machine learning task by clustering and selecting closest-to-the-centroid objects,” Herald of Khmelnytskyi National University. Technical Sciences, vol. 1, no. 6, pp. 263–265, 2018.Search in Google Scholar
R. Ostrovsky, Y. Rabani, L. J. Schulman, and C. Swamy, “The effectiveness of Lloyd-type methods for the k-means problem,” in Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06), Berkeley, CA, USA, Oct. 2006, pp. 165–174. https://doi.org/10.1109/FOCS.2006.75Search in Google Scholar
H. A. Yehoshyna and V. V. Romanuke, “Constraint-based recommender system for commodity realization,” Journal of Communications Software and Systems, vol. 17, no. 4, pp. 314–320, Oct. 2021. https://doi.org/10.24138/jcomss-2021-0102Search in Google Scholar
A. Vattani, “k-means requires exponentially many iterations even in the plane,” Discrete and Computational Geometry, vol. 45, no. 4, pp. 596–616, Mar. 2011. https://doi.org/10.1007/s00454-011-9340-1Search in Google Scholar
A. Chakrabarty and D. Swagatam, “On strong consistency of kernel k-means: A Rademacher complexity approach,” Statistics & Probability Letters, vol. 182, Mar. 2022, Art. no. 109291. https://doi.org/10.1016/j.spl.2021.109291Search in Google Scholar
J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A k-means clustering algorithm,” Journal of the Royal Statistical Society, Series C, vol. 28, no. 1, pp. 100–108, 1979. https://doi.org/10.2307/2346830Search in Google Scholar
J. Cartensen, “About hexagons,” Mathematical Spectrum, vol. 33, no. 2, pp. 37–40, 2000–2001.Search in Google Scholar
R. Fletcher, Practical Methods of Optimization (2nd ed.). J. Wiley and Sons, Chichester, 1987.Search in Google Scholar
S. A. Vavasis, “Complexity issues in global optimization: A survey,” in Handbook of Global Optimization. Nonconvex Optimization and Its Applications, R. Horst and P. M. Pardalos, Eds., vol. 2. Springer, Boston, MA, 1995, pp. 27–41. https://doi.org/10.1007/978-1-4615-2025-2_2Search in Google Scholar