Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31014
A Constrained Clustering Algorithm for the Classification of Industrial Ores

Authors: Luciano Nieddu, Giuseppe Manfredi


In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures the information on the closed two-dimensional contour of an image, on images of industrial mineral ores.

Keywords: supervised classification, k-means, Industrial ores classification, Invariant Features

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1044


[1] L. Nieddu and G. Patrizi, Formal methods in pattern recognition: a review. European Journal of Operational Research. v120. 459-495, 2000.
[2] J. Yokono and T. Poggio, Oriented filters for objects recognition: an empirical study, Proceedings of the IEEE Conference on Face and Gesture Recognition (FG2004). Seoul, Korea, 2004.
[3] V. N. Vapnik. The nature of statistical learning theory Springer-Verlag, Berlin, 1995.
[4] R. O. Duda and P. E. Hart. Pattern Classification and scene analysis Wiley, New York, 1973.
[5] S. Watanabe. Pattern Recognition: human and mechanical Wiley, New York, 1985.
[6] A. Khotanzad and Y. H. Hong. Rotation invariant image recognition using features selected via a systematic method. Pattern Recognition, 23(10) pp. 1089-1101, 1990.
[7] D. G. Lowe. Object recongition from local scale invariant fetures. In ICCV, pp. 1150-1157, 1999.
[8] K. Fukunaga. Introduction to statistical pattern recognition. Academic Press, 1990.
[9] D. Gordon. Classification. Chapman & Hall Ltd., London, New York, 1999
[10] L. Nieddu and G. Patrizi. Optimization and algebraic techniques for image analysis. In M. Lassonde, editor, Approximation, optimization and Mathematical Economics, pp. 235-242. Physica-Verlag, 2001
[11] J. W. Cooley, P. A. Lewis and P. D. Welch. The finite Fourier transform. IEEE Trans. Audio electroacoustics, 17(2) pp. 77-85, 1969.
[12] R. Bellman. Adaptive control Processes: a guided tour. Princeton University Press, 1961.
[13] J. W. Cooley, P. A. Lewis and P. D. Welch. The fast Fourier transform and its application to time series analysis. In Statistical Methods for digital computers, Wiley, New York, 1977.
[14] E. O. Brigham. the fast Fourier transform and its applications. Englewood Cliffs, N.J.: Prentice hall, 1988.
[15] G. J. McLachlan. Discriminant Analysis ans Statistical Pattern Recognition. John wiley & Sons, 1992.
[16] S. J. Haberman. Analysis of qualitative data. Volume 1: introductory topics, New York, Academic Press Inc, 1978.
[17] Y. M. M. Bishop, S. E. Fienberg and P. W. Holland. Discrete myultivariate analysis: theory and practice, Cambridge, Massachusset, The MIT press, 1975.
[18] A. Agresti. Analysis of ordinal data Wiley, New York, 1984.
[19] S. watnabe. Karhunen-Lo'eve expansion and factor analysis. Theoretical remarks and applications. Transactions of the fourth Prague conference on Information Theory, Statistical Decision functions, Random Processes, pp. 635-660, 1965