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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2017 - 1
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Centroid Update Approach to K-Means Clustering

BORLEA, I.-D. See more information about BORLEA, I.-D. on SCOPUS See more information about BORLEA, I.-D. on IEEExplore See more information about BORLEA, I.-D. on Web of Science, PRECUP, R.-E. See more information about  PRECUP, R.-E. on SCOPUS See more information about  PRECUP, R.-E. on SCOPUS See more information about PRECUP, R.-E. on Web of Science, DRAGAN, F. See more information about  DRAGAN, F. on SCOPUS See more information about  DRAGAN, F. on SCOPUS See more information about DRAGAN, F. on Web of Science, BORLEA, A.-B. See more information about BORLEA, A.-B. on SCOPUS See more information about BORLEA, A.-B. on SCOPUS See more information about BORLEA, A.-B. on Web of Science
 
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Download PDF pdficon (1,203 KB) | Citation | Downloads: 632 | Views: 519

Author keywords
clustering algorithms, clustering methods, data analysis, data mining, machine learning algorithms

References keywords
data(12), fuzzy(9), algorithms(9), systems(7), control(7), comput(7), optimal(6), clustering(6), algorithm(6), system(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04001
Web of Science Accession Number: 000417674300001
SCOPUS ID: 85035816652

Abstract
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The volume and complexity of the data that is generated every day increased in the last years in an exponential manner. For processing the generated data in a quicker way the hardware capabilities evolved and new versions of algorithms were created recently, but the existing algorithms were improved and even optimized as well. This paper presents an improved clustering approach, based on the classical k-means algorithm, and referred to as the centroid update approach. The new centroid update approach formulated as an algorithm and included in the k-means algorithm reduces the number of iterations that are needed to perform a clustering process, leading to an alleviation of the time needed for processing a dataset.


References | Cited By  «-- Click to see who has cited this paper

[1] C. Eaton, P. Zikopoulos, T. Deutsch, G. Lapis, and D. Deroos, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. New York: McGraw-Hill, 2012.

[2] A. Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil, A. Y. Zomaya, S. Foufou, and A. Bouras, "A survey of clustering algorithms for big data: taxonomy and empirical analysis," IEEE Trans. Emerg. Top. Comput., vol. 2, no. 3, pp. 267-279, Sep. 2014.
[CrossRef] [Web of Science Times Cited 89] [SCOPUS Times Cited 137]


[3] J. Mac Queen, "Some methods for classification and analysis of multivariate observations," in Proc. Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 1967, vol. 1, pp. 281-297.

[4] S. Lloyd, "Least squares quantization in PCM," IEEE Trans. Inf. Theory, vol. 28, pp. 129-137, Mar. 1982.
[CrossRef] [Web of Science Times Cited 3037] [SCOPUS Times Cited 4289]


[5] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. F. M. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, and D. Steinberg, "Top 10 algorithms in data mining," Knowl. Informat. Syst., vol. 14, no. 1, pp. 1-37, Jan. 2008.
[CrossRef] [Web of Science Times Cited 1244] [SCOPUS Times Cited 1655]


[6] J. Andreu-Perez, C. C. Y. Poon, R. D. Merrifield, S. T. C. Wong, and G.-Z. Yang, "Big data for health," IEEE J. Biomed. Health Inform., vol. 19, no. 4, pp. 1193-1208, July 2015.
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 86]


[7] S. Ram, W. L. Zhang, M. Williams, and Y. Pengetnze, "Predicting asthma-related emergency department visits using big data," IEEE J. Biomed. Health Inform., vol. 19, no. 4, pp. 1216-1223, July 2015.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 35]


[8] W. Breymann, A. Dias, and P. Embrechts, "Dependence structures for multivariate high-frequency data in finance," Quant. Finance, vol. 3, no. 1, pp. 1-14, 2003.
[CrossRef] [SCOPUS Times Cited 190]


[9] P. Dewdney, P. Hall, R. Schilizzi, and J. Lazio, "The square kilometre array," Proc. IEEE, vol. 97, no. 8, pp. 1482-1496, Aug. 2009.
[CrossRef] [Web of Science Times Cited 290] [SCOPUS Times Cited 329]


[10] C. Reed, D. Thompson, W. Majid, and K. Wagstaff, "Real time machine learning to find fast transient radio anomalies: A semi-supervised approach combining detection and RFI excision," in Proc. International Astronomical Union Symposium on Time Domain Astronomy, 2011, pp. 1-6.

[11] J. Erman, M. Arlitt, and A. Mahanti, "Traffic classification using clustering algorithms," in Proc. SIGCOMM Workshop on Mining Network Data, Pisa, Italy, 2006, pp. 281-286.

[12] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, "An efficient k-means clustering algorithm: analysis and implementation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881-892, Jul. 2002.
[CrossRef] [Web of Science Times Cited 1547] [SCOPUS Times Cited 2132]


[13] D. Arthur and S. Vassilvitskii, "k-means++: the advantages of careful seeding," in Proc. Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA, 2007, pp. 1027-1035.

[14] S. Nasser, R. Alkhaldi, and G. Vert, "A modified fuzzy k-means clustering using expectation maximization," in Proc. 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, 2006, pp. 231-235.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 25]


[15] K. A. A. Nazeer, S. D. M. Kumar, and M. P. Sebastian, "Enhancing the k-means clustering algorithm by using a O(n logn) heuristic method for finding better initial centroids," in Proc. 2011 Second International Conference on Emerging Applications of Information Technology, Washington, DC, USA, 2011, pp. 261-264.
[CrossRef] [SCOPUS Times Cited 11]


[16] D. Pelleg and A. Moore, "Accelerating exact k-means algorithms with geometric reasoning," in Proc. ACM SIGKDD Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 1999, pp. 277-281.

[17] C. Elkan, "Using the triangle inequality to accelerate k-means," in Proc. Twentieth International Conference on Machine Learning, Washington, DC, USA, 2003, pp. 147-153.

[18] A. W. Moore, "The anchors hierarchy: using the triangle inequality to survive high dimensional data," in Proc. Twelfth Conference on Uncertainty in Artificial Intelligence, CA, USA, 2000, pp. 397-405.

[19] T. Kaukoranta, P. Franti, and O. Nevalainen, "A fast exact GLA based on code vector activity detection," IEEE Trans. Image Process., vol. 9, no. 8, pp. 1337-1342, Aug. 2000.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 53]


[20] [Online] Available: Temporary on-line reference link removed - see the PDF document

[21] I.-D. Borlea, R.-E. Precup, and F. Dragan, "On the architecture of a clustering platform for the analysis of big volumes of data," in Proc. IEEE 11th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, 2016, pp. 145-150.
[CrossRef] [SCOPUS Times Cited 1]


[22] P. Baranyi, D. Tikk, Y. Yam, and R. J. Patton, "From differential equations to PDC controller design via numerical transformation," Comput. Ind., vol. 51, no. 3, pp. 281-297, Aug. 2003.
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 120]


[23] I. Skrjanc, S. Blazic, and O. E. Agamennoni, "Identification of dynamical systems with a robust interval fuzzy model," Automatica, vol. 41, no. 2, pp. 327-332, Feb. 2005.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 77]


[24] F. G. Filip, "Decision support and control for large-scale complex systems," Annual Rev. Control, vol. 32, no. 1, pp. 61-70, Apr. 2008.
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 102]


[25] D. Martín, R. Del Toro, R. Haber, and J. Dorronsoro, "Optimal tuning of a networked linear controller using a multi-objective genetic algorithm and its application to one complex electromechanical process," Int. J. Innov. Comput. Informat. Control, vol. 5, no. 10 (B), pp. 3405-3414, Oct. 2009.

[26] J. Vascak and M. Pala, "Adaptation of fuzzy cognitive maps for navigation purposes by migration algorithms," Int. J. Artif. Intell., vol. 8, no. S12, pp. 20-37, Oct. 2012.

[27] R.-E. Precup, R.-C. David, E. M. Petriu, S. Preitl, and M.-B. Radac, "Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers," Expert Syst. Appl., vol. 41, no. 4, pp. 1168-1175, Mar. 2014.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 53]


[28] D. Wijayasekara, O. Linda, M. Manic, and C. G. Rieger, "Mining building energy management system data using fuzzy anomaly detection and linguistic descriptions," IEEE Trans. Ind. Informat., vol. 10, no. 3, pp. 1829-1840, Aug. 2014.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 35]


[29] R.-E. Precup, M.-C. Sabau, and E. M. Petriu, "Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for anti-lock braking systems," Appl. Soft Comput., vol. 27, pp. 575-589, Feb. 2015.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 33]


[30] A. Y. Jaen-Cuellar, L. Morales-Velazquez, R. Romero-Troncoso, and R. A. Osornio-Rios, "FPGA-based embedded system architecture for micro-genetic algorithms applied to parameters optimization in motion control," Adv. Electr. Comput. Eng., vol. 15, no. 1, pp. 23-32, Mar. 2015.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[31] O. Arsene, I. Dumitrache, and I. Mihu, "Expert system for medicine diagnosis using software agents," Expert Syst. Appl., vol. 42, no. 4, pp. 1825-1834, Mar. 2015.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 15]


[32] A. Basgumus, M. Namdar, G. Yilmaz, and A. Altuncu, "Performance comparison of the differential evolution and particle swarm optimization algorithms in free-space optical communications systems," Adv. Electr. Comput. Eng., vol. 15, no. 3, pp. 17-22, Sep. 2015.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[33] A. Moharam, M. A. El-Hosseini, and H. A. Ali, "Design of optimal PID controller using NSGA-II algorithm and level diagram," Stud. Informat. Control, vol. 24, no. 3, pp. 301-308, Sep. 2015.

[34] E. Osaba, E. Onieva, F. Dia, R. Carballedo, P. Lopez, and A. Perallos, "A migration strategy for distributed evolutionary algorithms based on stopping non-promising subpopulations: A case study on routing problems," Int. J. Artif. Intell., vol. 13, no. 2, pp. 46-56, Oct. 2015.

[35] J. K. Tar, J. F. Bito, and I. J. Rudas, "Contradiction resolution in the adaptive control of underactuated mechanical systems evading the framework of optimal controllers," Acta Polyt. Hung., vol. 13, no. 1, pp. 97-121, Jan. 2016.
[CrossRef]


[36] S. B. Ghosn, F. Drouby, and H. M. Harmanani, "A parallel genetic algorithm for the open-shop scheduling problem using deterministic and random moves," Int. J. Artif. Intell., vol. 14, no. 1, pp. 130-144, Mar. 2016.

[37] C. I. González, P. Melin, J. R. Castro, O. Castillo, and O. Mendoza, "Optimization of interval type-2 fuzzy systems for image edge detection," Appl. Soft Comput., vol. 47, pp. 631-643, Oct. 2016.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14]


[38] A. Fakharian and R. Rahmani, "An optimal controlling approach for voltage regulation and frequency stabilization in islanded microgrid system," Control Eng. Appl. Informat., vol. 18, no. 4, pp.107-114, Dec. 2016.



References Weight

Web of Science® Citations for all references: 6,719 TCR
SCOPUS® Citations for all references: 9,398 TCR

Web of Science® Average Citations per reference: 172 ACR
SCOPUS® Average Citations per reference: 241 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-04-18 10:02 in 187 seconds.




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