Generic placeholder image

Recent Patents on Engineering

Author(s): Shradha Deshmukh* and Preeti Mulay

DOI: 10.2174/1872212118666230616091148

DownloadDownload PDF Flyer Cite As
BibPat: Quantum K-means Clustering with Incremental Enhancement

Article ID: e160623218039 Pages: 16

  • * (Excluding Mailing and Handling)

Abstract

One of the main areas of study within the broader paradigm of quantum machine learning is quantum clustering (QC). Considering the potential time and cost savings that solutions to realworld issues employing QC algorithms bring, in comparison to their classical methods, researchers have recently developed a keen interest in QC. With new algorithms and their applications being invented virtually every other day, this is still a highly young and fascinating area of research. Based on the background information provided, this work aims to analyze research and patent databases spanning twelve years (2010 to mid-2022) to identify and understand the publishing and patent trends in the field of QC. This study aims to study the topological analyses, important study areas, relationships, and collaboration patterns that distinguish traditional and developing research clusters. The graphical representation of the progress of publications and patents over time depends on such rigorous field mapping. This paper presents a comprehensive list of all the sources through the network, bibliometric and patentometric (BibPat) analysis, and future research scope in the QC. The top authors, universities, and research fields were listed after the primary and secondary keywords connected to the quantum K-means clustering algorithm in the analysis design. Reviewing the articles and then delving into the specifics of the patents will help us evaluate the total body of work on the quantum K-means clustering technique. Using the thorough BibPat tools and numerous research and patent databases like Scopus, IEEE, ACM, Google Scholar, Lens, Google Patents, and Espacenet, the analysis design displays the patents and journal papers that have been published. Additionally, it is crucial for later research because it aids in the identification of areas for current research interests and possible avenues for future study. QC offers various studies in disciplines from computer science to psychology. The Ministry of Education, China, produced most publications. Since 2014, the trend has been up, and experts continue studying the issue. The BibPat analysis shows that the Chinese National Natural Science Foundation has facilitated funding for cutting-edge research. In order to open the door for future research and investigation on the substantial amount of unstructured real-time data, the report concluded by proposing an incremental QC approach.

Graphical Abstract

[1]
S. Shukla, and S. Naganna, "A review on K-means data clustering approach", Int. J. Inf. Comput. Technol., vol. 4, no. 17, pp. 1847-1860, 2014.
[2]
K. Krishna, and M.M. Narasimha, "Genetic K-means algorithm", IEEE Trans. Syst. Man Cybern. B Cybern., vol. 29, no. 3, pp. 433-439, 1999.
[http://dx.doi.org/10.1109/3477.764879] [PMID: 18252317]
[3]
V. Faber, "Clustering and the continuous k-means algorithm", Los Alamos Science, vol. 22, no. 22, 1994.
[4]
K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, "Constrained k-means clustering with background knowledge", ICML ’01: Proceedings of the Eighteenth International Conference on Machine Learning, 2001, pp. 577-584.
[5]
S. Lloyd, M. Mohseni, and P. Rebentrost, "Quantum algorithms for supervised and unsupervised machine learning", ArXiv, pp. 1307-0411, 2013.
[http://dx.doi.org/10.48550/arXiv.1307.0411]
[6]
J. Burkardt, K-means clustering. Virginia Tech.", In: Advanced Research Computing Interdisciplinary Center for Applied Mathematics, 2009.
[7]
S. Behrouzi, Z.S. Sarmoor, K. Hajsadeghi, and K. Kavousi, "Predicting scientific research trends based on link prediction in keyword networks", J. Informetrics, vol. 14, no. 4, p. 101079, 2020.
[http://dx.doi.org/10.1016/j.joi.2020.101079]
[8]
M.B. Soley, A. Markmann, and V.S. Batista, "Classical optimal control for energy minimization based on diffeomorphic modulation under observable-response-preserving homotopy", J. Chem. Theory Comput., vol. 14, no. 6, pp. 3351-3362, 2018.
[http://dx.doi.org/10.1021/acs.jctc.8b00124] [PMID: 29677446]
[9]
K. Benlamine, Y. Bennani, N. Grozavu, and B. Matei, Quantum Collaborative K-means. 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-7.
Glasgow, UK [http://dx.doi.org/10.1109/IJCNN48605.2020.9207334]
[10]
I. Kerenidis, J. Landman, A. Luongo, and A. Prakash, "q-means: A quantum algorithm for unsupervised machine learning", ArXiv preprint ArXiv:1812.03584
[11]
A. Likas, N. Vlassis, and J.J. Verbeek, "The global k-means clustering algorithm", Pattern Recognit., vol. 36, no. 2, pp. 451-461, 2003.
[http://dx.doi.org/10.1016/S0031-3203(02)00060-2]
[12]
V. Kumar, G. Bass, C. Tomlin, and J. Dulny III, "Quantum annealing for combinatorial clustering", Quantum Inform. Process., vol. 17, no. 2, p. 39, 2018.
[http://dx.doi.org/10.1007/s11128-017-1809-2]
[13]
N. Wiebe, A. Kapoor, and K.M. Svore, "Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning", Quantum Inf. Comput., vol. 15, no. 3&4, pp. 316-356, 2015.
[http://dx.doi.org/10.26421/QIC15.3-4-7]
[14]
S. Deshmukh, and P. Mulay, Quantum clustering drives innovations: A bibliometric and patentometric analysis. Library Philosophy and Practice 2021.Pune, India, .
[15]
M. Hou, S. Zhang, and J. Xia, "Quantum fuzzy k-means algorithm based on fuzzy theory", In International Conference on Adaptive and Intelligent Systems, 2022, pp. 348-356
[http://dx.doi.org/10.1007/978-3-031-06794-5_28]
[16]
Z. Wu, T. Song, and Y. Zhang, "Quantum k-means algorithm based on Manhattan distance", Quantum Inform. Process., vol. 21, no. 1, p. 19, 2022.
[http://dx.doi.org/10.1007/s11128-021-03384-7]
[17]
Y. Zhang, G. Zhang, D. Zhu, and J. Lu, "Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics", J. Assoc. Inf. Sci. Technol., vol. 68, no. 8, pp. 1925-1939, 2017.
[http://dx.doi.org/10.1002/asi.23814]
[18]
J. Sun, W. Chen, W. Fang, X. Wun, and W. Xu, "Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization", Eng. Appl. Artif. Intell., vol. 25, no. 2, pp. 376-391, 2012.
[http://dx.doi.org/10.1016/j.engappai.2011.09.017]
[19]
Z. Zhang, L. Song, L. Cheng, J. Tan, and W. Yang, "Accelerated graphitization of PAN-Based carbon fibers: K + -Effected graphitization via laser irradiation", ACS Sustain. Chem.& Eng., vol. 10, no. 24, pp. 8086-8093, 2022.
[http://dx.doi.org/10.1021/acssuschemeng.2c02417]
[20]
L. Zhen, Z. Yi, X. Peng, and D. Peng, "Locally linear representation for image clustering", Electron. Lett., vol. 50, no. 13, pp. 942-943, 2014.
[http://dx.doi.org/10.1049/el.2014.0666]
[21]
A. Matsuoka, S.B. Hooker, A. Bricaud, B. Gentili, and M. Babin, "Estimating absorption coefficients of colored dissolved organic matter (CDOM) using a semi-analytical algorithm for southern Beaufort Sea waters: application to deriving concentrations of dissolved organic carbon from space", Biogeosciences, vol. 10, no. 2, pp. 917-927, 2013.
[http://dx.doi.org/10.5194/bg-10-917-2013]
[22]
K. Dong, W. Hu, and L. Lin, "Interpolative separable density fitting through centroidal voronoi tessellation with applications to hybrid functional electronic structure calculations", J. Chem. Theory Comput., vol. 14, no. 3, pp. 1311-1320, 2018.
[http://dx.doi.org/10.1021/acs.jctc.7b01113] [PMID: 29370521]
[23]
D. Nocito, and G.J.O. Beran, "Averaged condensed phase model for simulating molecules in complex environments", J. Chem. Theory Comput., vol. 13, no. 3, pp. 1117-1129, 2017.
[http://dx.doi.org/10.1021/acs.jctc.6b00890] [PMID: 28170251]
[24]
J. Zhang, and M. Dolg, "Third-order incremental dual-basis set zero-buffer approach for large high-spin open-shell systems", J. Chem. Theory Comput., vol. 11, no. 3, pp. 962-968, 2015.
[http://dx.doi.org/10.1021/ct501052e] [PMID: 26579750]
[25]
R.V. CasaAna-Eslava, "I.H. Jarman, P.J.G. Lisboa, and J.D. MartA-n-Guerrero, “Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters”", Neurocomputing, vol. 268, pp. 127-141, 2017.
[http://dx.doi.org/10.1016/j.neucom.2017.01.102]
[26]
Y. Li, Y. Wang, Y. Wang, L. Jiao, and Y. Liu, "Quantum clustering using kernel entropy component analysis", Neurocomputing, vol. 202, pp. 36-48, 2016.
[http://dx.doi.org/10.1016/j.neucom.2016.03.006]
[27]
Y. Cui, J. Shi, and Z. Wang, "Lazy Quantum clustering induced radial basis function networks (LQC-RBFN) with effective centers selection and radii determination", Neurocomputing, vol. 175, pp. 797-807, 2016.
[http://dx.doi.org/10.1016/j.neucom.2015.10.091]
[28]
Y. Li, H. Shi, L. Jiao, and R. Liu, "Quantum evolutionary clustering algorithm based on watershed applied to SAR image segmentation", Neurocomputing, vol. 87, pp. 90-98, 2012.
[http://dx.doi.org/10.1016/j.neucom.2012.02.008]
[29]
A. Sarma, R. Chatterjee, K. Gili, and T. Yu, "Quantum unsupervised and supervised learning on superconducting processors", ArXiv preprint ArXiv:1909.04226, 2019.
[30]
C. Shao, "Quantum speedup of training radial basis function networks", Quantum Inf. Comput., vol. 19, no. 7&8, pp. 609-625, 2019.
[http://dx.doi.org/10.26421/QIC19.7-8-6]
[31]
N. Wiebe, A. Kapoor, and K. Svore, "Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning", ArXiv preprint ArXiv:1401.2142, 2014.
[32]
W. Jie, and W. Yan, "Quality control method based on quantum genetic clustering algorithm", J. Sys. Simul., vol. 31, no. 12, p. 2591, 2019.
[33]
Z. Li, and P. Li, "Clustering algorithm of quantum self-organization network", Ozean J. Appl. Sci., vol. 5, no. 6, pp. 270-278, 2015.
[http://dx.doi.org/10.4236/ojapps.2015.56028]
[34]
J. Xiao, Y. Yan, J. Zhang, and Y. Tang, "A quantum-inspired genetic algorithm for k-means clustering", Expert Syst. Appl., vol. 37, no. 7, pp. 4966-4973, 2010.
[http://dx.doi.org/10.1016/j.eswa.2009.12.017]
[35]
S.I. Boushaki, N. Kamel, and O. Bendjeghaba, "A new quantum chaotic cuckoo search algorithm for data clustering", Expert Syst. Appl., vol. 96, pp. 358-372, 2018.
[http://dx.doi.org/10.1016/j.eswa.2017.12.001]
[36]
J. Sun, W. Xu, and B. Ye, "Quantum-behaved particle swarm optimization clustering algorithm", In: X. Li, O.R. Zaane, and Z. Li, Eds., Advanced Data Mining and Applications., vol. 4093. Springer: Berlin, Heidelberg, 2006.
[http://dx.doi.org/10.1007/11811305_37]
[37]
Z. Song, J. Peng, C. Li, and P.X. Liu, "A simple brain storm optimization algorithm with a periodic quantum learning strategy", IEEE Access, vol. 6, pp. 19968-19983, 2018.
[http://dx.doi.org/10.1109/ACCESS.2017.2776958]
[38]
Horn David, Gottlieb Assaf, and Axel Inon, "Method of and apparatus of quantum clustering", WO Patent 2002/093810 A2, 2008.
[39]
Sumsam Ullah Khan, Ahsan Javed Awan, and Gemma Vall-Llosera, "Classifying Data", US Patent 2022/0253649A1, 2017.
[40]
S. Deshmukh, and P. Mulay, "Quantum clustering algorithm using the wheel of tomography", Int. J. Eng. Trends Technol, vol. 70, no. 5, pp. 111-119, 2022.
[http://dx.doi.org/10.14445/22315381/IJETT-V70I5P214]
[41]
L.J. Kozlowski, R.B. Bailey, S.A. Cabelli, D.E. Cooper, G.D. McComas, K. Vural, and W.E. Tennant, "640 x 480 PACE HgCdTe FPA", In: Infrared Detectors: State of the Art,, vol. 1735. International Society for Optics and Photonics, 1735, pp. 163-174.
[http://dx.doi.org/10.1117/12.138620]