| 3/2011 - 13 |
Application of Rosette Pattern for Clustering and Determining the Number of ClusterSADR, A.   , MOMTAZ, A. K. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (2,657 KB) | Citation | Downloads: 1,477 | Views: 5,279 |
Author keywords
clustering, Fuzzy C-means (FCM), pattern recognition, Rosette Pattern, validity index
References keywords
clustering(17), pattern(12), fuzzy(11), algorithms(8), recognition(7), data(7), analysis(7), rosette(5), hall(5), clusters(5)
Blue keywords are present in both the references section and the paper title.
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
Abstract
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. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] A. K. Jain, R. C. Dubes, Algorithms for clustering data, Prentice-Hall, Englewood Cliffs, NJ, 1988.
[2] P. H. A. Sneath, R. R. Sokal, Numerical taxonomy, Freeman, San Francisco, London, 1973. [3] B. King, Step-wise clustering procedures, J. Am. Statist. Assoc. vol. 69, pp. 86-101, 1967. [CrossRef] [4] 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. [5] B. S. Everitt and D. J. Hand, Finite mixture distributions, London, U.K.: Chapman and Hall, 1981. [6] G. H. Ball, D. I. Hall, "ISODATA- A novel method of data analysis and classification," Stanford Res. Inst., California, 1965. [7] E. W. Forgy, "Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications," Biometrics, vol 21, pp. 768-769, 1965. [8] 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 156] [SCOPUS Times Cited 183] [9] M. Steinbach, G. Karypis, V. Kumar, "A comparison of document clustering techniques," KDD Workshop on Text Mining, 2000. [10] 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. [11] P. S. Bradley, U. Fayyad, C. Reina, "Scaling clustering algorithms to large databases," Proc. 4th KDD.1998. [12] 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. [13] L. Kaufman, P. J. Rousseeuw, "Finding groups in data: An introduction to cluster analysis," Wiley series in Probability and Statistics, 2005. [14] 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 5993] [SCOPUS Times Cited 7113] [15] 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 5050] [16] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, 1981. [17] 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. [18] 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 315] [SCOPUS Times Cited 360] [19] 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 489] [SCOPUS Times Cited 624] [20] 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 963] [SCOPUS Times Cited 1158] [21] 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 538] [SCOPUS Times Cited 664] [22] 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 41] [SCOPUS Times Cited 53] [23] 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 38] [SCOPUS Times Cited 48] [24] 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 21] [SCOPUS Times Cited 31] [25] 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 921] [SCOPUS Times Cited 1179] [26] 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 9808] [SCOPUS Times Cited 12606] [27] S. X. Yu, J. Shi, "Multiclass spectral clustering," Proc. Int. Conf. on Computer Vision, pp. 313-319, 2003. [CrossRef] [Web of Science Times Cited 489] [SCOPUS Times Cited 834] [28] 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. [29] 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. [30] 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. [31] 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] [32] 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 5717] [33] 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 17] [SCOPUS Times Cited 21] [34] S. Haykin, Neural Networks- A comprehensive foundation, New Jersey: Prentice Hall, 1999. [35] 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 18] [SCOPUS Times Cited 26] [36] 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. [37] 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 14] [SCOPUS Times Cited 19] [38] 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. [39] 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. [40] H. J. Zimmermann, Fuzzy Set Theory and Its Applications, Norwell, USA: Kluwer Academic publishers, 1996. [41] J. C. Bezdek, Pattern recognition in handbook of fuzzy computation, IOP Publishing Ltd., Boston, MA, 1998. Web of Science® Citations for all references: 19,821 TCR SCOPUS® Citations for all references: 35,688 TCR Web of Science® Average Citations per reference: 483 ACR SCOPUS® Average Citations per reference: 870 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 2024-12-16 22:12 in 143 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
