Statistical and fuzzy clustering methods and their application to clustering provinces of Iraq based on agricultural products

Document Type : Original Article

Authors

1 Faculty of Science, University of Al-Qadisiyah, Iraq

2 School of Engineering Science, College of Engineering, University of Tehran

Abstract

The important approaches to statistical and fuzzy clustering are reviewed and compared, and their applications to an agricultural problem based on a real-world data are investigated. The methods employed in this study includes some hierarchical clustering and non-hierarchical clustering methods and Fuzzy C-Means method. As a case study, these methods are then applied to cluster 15 provinces of Iraq based on some agricultural crops. Finally, a comparative and evaluation study of different statistical and fuzzy clustering methods is performed. The obtained results showed that, based on the Silhouette criterion and Xie-Beni index, fuzzy c-means method is the best one among all reviewed methods

Keywords


[1] Y. Al-Fahad and T. Abbas, GIS Center Central Bureau of Statistics (NBS), Iraq, 2011.
[2] N. Aguilar-Gallegos, M. Munoz-Rodriguez, H. Santoyo-Cortes, J. Aguilar-Avila, and L. Klerkx, Information Networks that Generate Economic Value: A Study on Clusters of Adopters of New or Improved Technologies and Practices among Oil Palm Growers in Mexico, Agricultural Systems, vol. 135, pp. 122-132, 2015.
[3] A. Ansari, P. S. Sikarwar, S. Lade, H. K. Yadav and S. A. Randade, Genetic Diversity in Germplasm of Cluster Bean, an Important Food and an Industrial Legume Crop, J. Agr. Sci. Tech., vol. 18, pp. 1393-1406, 2016.
[4] D. J. Bora and A. K. Gupta, A Comparative Study between Fuzzy Clustering Algorithm and Hard Clustering Algorithm, International Journal of Computer Trends and Technology, vol. 10, pp. 785-790, 2014.
[5] C. T. Chang, J. Z. C. Lai and M. D. Jeng, A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement, Journal of Information Science and Engineering, vol. 27, pp. 995-1009, 2011.
[6] S. Chattopadhyay, D. K. Pratihar, S. C. D Sarkar, A Comparative Study of Fuzzy C-Means Algorithm and Entropy Based Fuzzy Clustering Algorithms, Computing and Informatics, vol.30, pp. 701–720, 2011.
[7] R. Dave, Fuzzy Shell-Clustering and Applications to Circle Detection in Digital Images, lnt. J. General Systems, vol. 16,. pp. 343-355, 1989.
[8] J. V. De Oliveira and W. Pedrycz, Advances in Fuzzy Clustering and Its Applications, John Wiley and Sons, New York, 2007.
[9] M. Fajardo, A. Mc. Bratney and Whelan, Fuzzy Clustering of Vis–NIR Spectra for the Objective Recognition of Soil Morphological Horizons in Soil Profiles, Geoderma, vol. 263, pp. 244–253, 2016.
[10] M. B. Ferraro, and P. Giordani, A Toolbox for Fuzzy Clustering Using the R Programming Language, Fuzzy Sets and Systems, vol. 279, pp. 1–16, 2015.
[11] G. Gan, C. Ma and J. Wu, Data Clustering Theory, Algorithms, and Applications, SIAM, Virginia, 2007.
[12] C. Gomathi and K. Velusamy, Solving Fuzzy Clustering Problem Using Hybridization of Fuzzy C-Means and Fuzzy Bee Colony Optimization, International Journal of Computer Engineering and Applications, vol. 12, pp. 317–324, 2018.
[13] N. Grover, A Study of Various Fuzzy Clustering Algorithms, International Journal of Engineering Research, vol. 3, pp. 177–181, 2014.
[14] Z. Huang and M. Ng, A Fuzzy K-Modes Algorithm for Clustering Categorical Data. IEEE Transactions on Fuzzy Systems, vol.7, pp.446–452, 1999.
[15] J. G. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, New York, 1995.
[16] P. Kostov and S. Mc Erlean, Using the Mixtures- of Distributions Technique for the Classification of Farms into Representative Farms. Agricultural Systems, vol. 88, pp. 528-537, 2006.
[17] E. Mansour, A. B. Khaled, T. Triki, M. Abid, K. Bachar, and A. Ferchichi, Evaluation of Genetic Diversity among South Tunisian Pomegranate Accessions Using Fruit Traits and RAPD Markers. J. Agr. Sci. Tech., vol. 17, pp. 109-119, 2015.
[18] B. Panda, S. Sahoo, and S. K. Patnaik, A Comparative Study of Hard and Soft Clustering Using Swarm Optimization: International Journal of Scientific & Engineering Research, vol. 4, pp. 785- 790, 2013.
[19] P. J. Rousseeuw, Silhouettes A Graphical Aid to the Interpretation and Validation of Cluster Analysis, Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.
[20] A. C. Rencher, Methods of Multivariate Analysis, John Wiley and Sons, New York, 2002.
[21] H. Timm, C. Borgelt, C. Doring, and R. Kruse, An Extension to Possibilistic Fuzzy Cluster Analysis, Fuzzy Sets and Systems, 147, 3–16, 2004.
[22] T. Volmurgan, Austria Performance Comparison Between K-means and Fuzzy C- means, Wulfenia Journal Using Arbitrary Data Points, vol. 19, pp. 1-8, 2012.
[23] R. Suganya, and R. Shanthi, Fuzzy C- Means Algorithm - A Review, International Journal of Scientific and Research Publications, vol. 2, pp. 440-442, 2012.
[24] X.L. Xie, and G. Beni, A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 8, pp. 841-847, 1991.
[25] M. S. Yang, Convergence Properties of the Generalized Fuzzy C-Means Clustering Algorithms, Computer Math. Appl, vol. 25, pp. 3-11, 1993.