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Application of Rosette Pattern for Clustering and Determining the Number of ClusterSADR, A. , MOMTAZ, A. K.
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clustering, Fuzzy C-means (FCM), pattern recognition, Rosette Pattern, validity index
clustering(17), pattern(12), fuzzy(11), algorithms(8), recognition(7), data(7), analysis(7), rosette(5), hall(5), clusters(5)
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About this article
Date of Publication: 2011-08-31
Volume 11, Issue 3, Year 2011, On page(s): 77 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.03013
Web of Science Accession Number: 000296186700013
SCOPUS ID: 80055116504
Clustering is one of the most important research topics which has many practical applications such as medical imaging and Non-Destructive Testing (NDT). Most clustering algorithms like K-means, fuzzy C-Means (FCM) and their derivatives require the number of clusters as one of the initializing parameters. This paper proposes an algorithm for image clustering with no need to any initializing parameter. In this state-of-the-art, an image is sampled based on a rosette pattern and according to the pattern characteristics, the extracted samples are clustered and then the number of clusters is determined. The centroids of classes are computed by means of a method based on calculation of distribution function. Based on different data sets, the results show that the algorithm improves the capability of the clustering by a minimum of 62.26% and 87.62% in comparison with FCM and K-means algorithms, respectively. Moreover, in dealing with high resolution data sets, the efficiency of the algorithm in clusters detection and run time improvement increases considerably.
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| A. K. Jain, R. C. Dubes, Algorithms for clustering data, Prentice-Hall, Englewood Cliffs, NJ, 1988.
 P. H. A. Sneath, R. R. Sokal, Numerical taxonomy, Freeman, San Francisco, London, 1973.
 B. King, Step-wise clustering procedures, J. Am. Statist. Assoc. vol. 69, pp. 86-101, 1967.
 J. MacQueen, "Some methods for classification and analysis of multivariate observations," Fifth Berkeley Symposium on Mathematics, Statistics and Probability, University of California Press, pp. 281-297, 1967.
 B. S. Everitt and D. J. Hand, Finite mixture distributions, London, U.K.: Chapman and Hall, 1981.
 G. H. Ball, D. I. Hall, "ISODATA- A novel method of data analysis and classification," Stanford Res. Inst., California, 1965.
 E. W. Forgy, "Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications," Biometrics, vol 21, pp. 768-769, 1965.
 S. Eschrich, K. Jingwei, L. O. Hall, D. B. Goldgof, "Fast accurate fuzzy clustering through data reduction," IEEE Trans. Fuzzy Systems, vol. 11, no. 2, pp. 262-270, 2003.
[CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 138]
 M. Steinbach, G. Karypis, V. Kumar, "A comparison of document clustering techniques," KDD Workshop on Text Mining, 2000.
 D. Pelleg, A. Moore, "Accelerating exact k-means algorithms with geometric reasoning," Proc. Fifth Internat. Conf. on Knowledge Discovery in Databases, AAAI Press, pp. 277-281, 1999.
 P. S. Bradley, U. Fayyad, C. Reina, "Scaling clustering algorithms to large databases," Proc. 4th KDD.1998.
 D. Pelleg, A. Moore, "X-means: Extending k-means with efficient estimation of the number of clusters," 17th Int. Conf. on Machine Learning. pp. 727-734, 2000.
 L. Kaufman, P. J. Rousseeuw, "Finding groups in data: An introduction to cluster analysis," Wiley series in Probability and Statistics, 2005.
 A. K. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, pp. 651-666, 2010.
[CrossRef] [Web of Science Times Cited 1904] [SCOPUS Times Cited 2493]
 J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," J. Cyberne, vol. 3, pp. 32-57, 1973.
[CrossRef] [SCOPUS Times Cited 2975]
 J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, 1981.
 H. Sun, S. Wang, Q. Jiang, "FCM-based model selection algorithms for determining the number of clusters," Pattern Recognition Society, vol. 37, no. 10, pp. 2027-2037, 2004.
 A. Baraldi, P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition- part I," IEEE Trans. Syst. Man, Cybern. B, vol. 29, no. 6, pp. 778-785, 1999.
[CrossRef] [PubMed] [Web of Science Times Cited 216] [SCOPUS Times Cited 254]
 E. R. Hruschka, R. J. G. B. Campello, A. A. Freitas, and A. de Carvalho, "A survey of evolutionary algorithms for clustering," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 39, no. 2, pp. 133-155, March 2009.
[CrossRef] [Web of Science Times Cited 268] [SCOPUS Times Cited 362]
 U. Maulik, S. Bandyopadhyay, "Performance evaluation of some clustering algorithms and validity indices," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1650-1654, 2002.
[CrossRef] [Web of Science Times Cited 493] [SCOPUS Times Cited 613]
 M. K. Pakhira, U. Maulik, and S. Bandyopadhyay, "Validity index for crisp and fuzzy clusters," Pattern Recognition, vol. 37, no. 3, pp. 487-501, 2004.
[CrossRef] [Web of Science Times Cited 282] [SCOPUS Times Cited 381]
 S. M. Pan and K. S. Cheng, "Evolution-based tabu search approach to automatic clustering," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 5, pp. 827-838, Sep. 2007.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 38]
 Y. Wang, C. Li, and Y. Zuo, "A Selection model for optimal fuzzy clustering algorithm and number of clusters based on competitive comprehensive fuzzy evaluation," IEEE Tran. on Fuzzy Systems, vol. 17, (3), pp. 568-577, 2009.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 37]
 S. Saha and S. Bandyopadhyay, "Performance evaluation of some symmetry-based cluster validity indexes," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 39, no. 4, pp. 420-425, Jul. 2009.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 20]
 C. Fowlkes, S. Belongie, F. Chung, and J. Malik, "Spectral grouping using the nystrom method," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 214-225, Feb. 2004.
[CrossRef] [PubMed] [Web of Science Times Cited 503] [SCOPUS Times Cited 798]
 J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[CrossRef] [Web of Science Times Cited 5620] [SCOPUS Times Cited 8109]
 S. X. Yu, J. Shi, "Multiclass spectral clustering," Proc. Int. Conf. on Computer Vision, pp. 313-319, 2003.
 M. Belkin, P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," Advances in Neural Information Processing Systems, vol. 14, pp. 585-591, 2002.
 W. Y. Chen, Y. Song, H. Bai, C J. Lin, and E. Y. Chang, "Parallel spectral clustering in distributed systems", IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 33, no. xx, 2011, to be published.
 R. M. Gray, J. C. Young, and A. K. Aiyer, "Minimum discrimination information clustering: modeling and quantization with Gauss mixtures," Proc. Int. Conf. Image Processing, vol. 3, pp. 14-17, 2001.
 K. M. Ozonat and R. M. Gray, "Guass mixture image classification for the linear image transforms," IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 5, pp. v/337 - v/340, 2005.
[CrossRef] [SCOPUS Times Cited 2]
 R. P., Lippman, "An introduction to computing with neural nets," ASSP Magazine, IEEE, vol. 4, no.2, pp. 4-22, 1987.
[CrossRef] [SCOPUS Times Cited 4335]
 S. Liu, C. Ume, and A. Achari, "Defects pattern recognition for flip-chip solder joint quality inspection with laser ultrasound and interferometer," IEEE transactions on electronics packing manufacturing, vol. 27, no. 1, pp. 59-66, 2004.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 17]
 S. Haykin, Neural Networks- A comprehensive foundation, New Jersey: Prentice Hall, 1999.
 S. G., Jahng, H. K., Hong, and J. S. Choi, "Dynamic simulation of the rosette scanning infrared seeker and an IRCCM using the moment technique," Optical Engineering, vol. 38, no. 5, pp. 921-928, 1999.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 23]
 S. G. Jahng, H. K. Hong, and J. S. Choi, "Simulation of rosette infrared seeker and counter-countermeasure using K-means algorithm," IEICE Tran. on Fundamentals of Electronics, Communications and Computer Sciences, vol. E82-A, no. 6, pp. 987-993, 1999.
 S. G. Jahng, H. K. Hong, D. S. Seo, and J. S. Choi, "New infrared counter-countermeasure technique using an iterative self-organizing data algorithm for the rosette scanning infrared seeker," Optical Engineering, vol. 39, no. 9, pp. 2397-2404, 2000.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 14]
 S. G. Jahng, H. K. Hong, J. S. Choi, "Clustering method for rosette scan images," US patent, number 6,807,307 B2, Oct. 19, 2004.
 S. B. Shokouhi, A. K. Momtaz, H. Soltanizadeh, "The new weighting and clustering methods for the rosette pattern," WSEAS Transactions on information science & applications, vol. 2, no. 9, pp. 1250-1257, 2005.
 H. J. Zimmermann, Fuzzy Set Theory and Its Applications, Norwell, USA: Kluwer Academic publishers, 1996.
 J. C. Bezdek, Pattern recognition in handbook of fuzzy computation, IOP Publishing Ltd., Boston, MA, 1998.
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