Comparison of Clustering Methods in Cotton Textile Industry

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)


Clustering is the task of partitioning data objects into groups, so that the objects within a cluster are similar to one another and dissimilar to the objects in other clusters. The efficiency random algorithm for good k is used to estimate the optimal number of clusters. In this research two important clustering algorithms, namely centroid based k-means, and representative object based fuzzy c-means clustering algorithms are compared in the original real-world U.S. cotton textile and apparel imports data set. This data set is not analyzed very often, it is dictated by business, economics and politics environments and its behaviour is not well known. The analysis of several different real-world economies and industrial data sets of one country is possible to predict it’s economic development.


Data clustering Number of clusters k-means algorithm Fuzzy c-means Random algorithm 



The authors acknowledge the support for research project TR 36030, funded by the Ministry of Science and Technological Development of Serbia.


  1. 1.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)zbMATHGoogle Scholar
  2. 2. Accessed 29 April 2015
  3. 3.
    Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, New York (2005)zbMATHGoogle Scholar
  4. 4.
    Spath, H.: Cluster Analysis Algorithms. Ellis Horwood, Chichester (1980)zbMATHGoogle Scholar
  5. 5.
    Han, J., Kamber, M.: Data Mining. Morgan Kaufmann Publishers, Burlington (2001)zbMATHGoogle Scholar
  6. 6.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)zbMATHGoogle Scholar
  7. 7.
    Jain, A.K., Murty, N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  8. 8.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Gersho, A., Gray, R.M.: Vector quantization and Signal Compression. Communications and Information Theory. Kluwer Academic Publishers, Norwell (1992)CrossRefzbMATHGoogle Scholar
  10. 10.
    Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: 6th ACM SIGKDD, World Text Mining Conference, Boston (2000)Google Scholar
  11. 11.
    Ester, M., Frommlet, A., Kriegel, H.P., Sander, J.: Spatial data mining: database primitives, algorithms and efficient DBMS support. Data Min. Knowl. Discov. 4(2–3), 193–216 (2000)CrossRefGoogle Scholar
  12. 12.
    Heer, J., Chi, E.: Identification of web user traffic composition using multimodal clustering and information scent. In: 1st SIAM ICDM, Workshop on Web Mining, Chicago, pp. 51–58 (2001)Google Scholar
  13. 13.
    Petrov, N., Georgieva, A., Jordanov, I.: Self-organizing maps for texture classification. Neural Comput. Appl. 22(7–8), 1499–1508 (2013)CrossRefGoogle Scholar
  14. 14.
    Tibshirani, R., Hastie, T., Eisen, M., Ross, D., Botstein, D., Brown, P.: Clustering methods for the analysis of DNA microarray data. Department of Statistics, Stanford University, Stanford, Technical report. Accessed 29 April 2015
  15. 15.
    Piórkowski, A., Gronkowska–Serafin, J.: Towards precise segmentation of corneal endothelial cells. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015, Part I. LNCS, vol. 9043, pp. 240–249. Springer, Heidelberg (2015)Google Scholar
  16. 16.
    Bigus, J.P.: Data Mining with Neural Networks. McGraw-Hill, New York (1996)Google Scholar
  17. 17.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)zbMATHGoogle Scholar
  18. 18.
    Mecca, G., Raunich, S., Pappalardo, A.: A New algorithm for clustering search results. Data Knowl. Eng. 62(3), 504–522 (2007)CrossRefGoogle Scholar
  19. 19.
    Valafar, F.: Pattern recognition techniques in microarray data analysis: a survey. Ann. N. Y. Acad. Sci. 980, 41–64 (2002)CrossRefGoogle Scholar
  20. 20.
    Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng. 16(11), 1370–1386 (2004)CrossRefGoogle Scholar
  21. 21.
    Das, N.: Hedge fund classification using k-means clustering method. In: 9th International Conference on Computing in Economics and Finance (2003) Accessed 25 June 2015
  22. 22.
    Shi, W., Zeng, W.: Application of k-means clustering to environmental risk zoning of the chemical industrial area. Front. Environ. Sci. Eng. 8(1), 117–127 (2014)CrossRefGoogle Scholar
  23. 23.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)zbMATHGoogle Scholar
  24. 24.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, New York (1981)CrossRefzbMATHGoogle Scholar
  25. 25.
    Akaike, H.: A new look at statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50(2), 159–179 (1985)CrossRefGoogle Scholar
  28. 28.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. 63(2), 411–423 (2001)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia
  2. 2.Department of Systems and Computer NetworksWroclaw University of TechnologyWrocławPoland
  3. 3.Faculty of MedicineUniversity of Novi SadNovi SadSerbia

Personalised recommendations