The week's pick
Random Articles
Reseach Article
Autonomous Wheat Seed Type Classifier System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 96 - Number 12 |
| Year of Publication: 2014 |
| Authors: Ahmad Reza Parnian, Reza Javidan |
10.5120/16845-6702
|
Ahmad Reza Parnian, Reza Javidan . Autonomous Wheat Seed Type Classifier System. International Journal of Computer Applications. 96, 12 ( June 2014), 14-17. DOI=10.5120/16845-6702
Abstract
After harvesting wheat, the main concern is classifying wheat seeds according to their quality, size, variety and etc. there are different procedures to measure parameters and analyzing wheat seeds but they are time-consuming and error-prone. An automated system is developed being capable to analyze and classify wheat seeds faster with higher confidence level based on defined attributes, the system uses popular K-means clustering algorithm. The base of K-means is established on squared error. Several points are given as inputs to algorithm and then they are assigned to k clusters according to distance to the centroids, each point is included in cluster which centroid is nearest to that point. A wheat dataset taken from UCI Machine Learning Repository is considered by k-means algorithm and results are analyzed. The experimental results on prototype data show the effectiveness of the proposed method.
References
- J. Wu, Advancs in K-means Clustering: A Data Mining Thinking, Springer, 2012.
- K. Alsabti, S. Ranka and V. Singh, "An Efficient k-means Clustering Algorithm", Proc, First Workshop High Performance Data Mining, Mar 1998.
- L. Lin and L. Suhua, "Wheat Cultivar Classifications Based on Tabu Search and Fuzzy C-means Clustering Algorithm", Fourth International Conference on Computational and Information Sciences, pp. 493-496, Aug 2012.
- D. Liying, Z. Genshan, L. Xuning, S. Wei, L. Aiqin and C. Weihua, "Design and Realization of Grain Seed Quality Testing System Based on Particle Image Processing Technology", International Conference on Computer Science and Electronics Engineering, vol. 3, pp. 61-65, March 2012.
- M. R. Neuman, E. Shwedyk and W. Bushuk, "A PC-based colour image processing system for wheat grain grading", International Conference on Image Processing and its Applications, pp. 242-246, Jul 1989.
- L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis, John Wiley & Sons, 1990.
- P. S. Bradley and U. Fayyad, "Refining Initial Points for K-means Clustering", Proc. 15th Int'l Conf. Machine Learning, pp. 91-99, 1998.
- http://www. mathworks. com,"StatisticsToolbox:K-means Clustering", r2013a.
- R. C. Dubes and A. K. Jain, Algorithms for Clustering Data, Prentice Hall, 1988.
- K. Wagsta, C. Cardie, S. Rogers and S. Schroedl, "Constrained K-means Clustering with Background Knowledge", Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577-584, 2001.
- S. Arora, P. Raghavan, and S. Rao, "Approximation Schemes for Euclidean k-median and Related Problems", Proc. 30th Ann. ACM Symp. Theory of Computing, pp. 106-113, May 1998.
- T. Kanungo, D. M. Mount, N. S. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu, "The Analysis of a Simple k-means Clustering Algorithm", sixteenth annual symposium on Computational geometry, pp. 100-109, Jan 2000.
- 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 Transactions on Pattern Analysis and Machine Intelligence, vol. 24, NO. 7, pp. 881-892, July 2002.
- V. Faber, "Clustering and the Continuous k-means Algorithm", Los Alamos Science, vol. 22, pp. 138-144, 1994.
Index Terms
Keywords