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A Dolphin Herding Inspired Fuzzy Data Clustering Model and Its Applications

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Abstract

In this world of overwhelming data, efficient techniques are required to maintain this gigantic bulk of data. One of the renowned methods to serve this purpose is data clustering and hence is the objective of this paper. An algorithm is proposed which imitates the concept of herding, i.e., how dolphins catch their prey. It is an intelligent technique as all the data points are considered for every possible solution, providing clustering and ideal centroids in a short time. This paper presents the working of the algorithm on real datasets and extracts the results. The results of the proposed algorithm are then compared with well-known fuzzy c-means in terms of clusters formed presenting number of data items in each cluster, simplicity and coverage. The later part of the paper presents the association analysis technique on the clusters formed by the proposed approach with final section showing the cross comparisons made with respect to before and after clustering of data and for inter- and intra-clusters.

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References

  1. Bailey, K.D.: Numerical taxonomy and cluster analysis. In: Typologies and Taxonomies, issue 102. Sage, California (1994)

  2. Tryon, C.R.: Cluster Analysis: Correlation Profile and Orthometric (Factor) Analysis for the Isolation of Unities in Mind and Personality. Edwards Brothers, Ann Arbor (1939)

    Google Scholar 

  3. Jeng, J.-T., Chuang, C.-C., Tseng, C.-C., Juan, C.-C.: Robust interval competitive agglomeration clustering algorithm with outliers. Int. J. Fuzzy Syst. 12(3), 227–236 (2010)

    Google Scholar 

  4. Miyamoto, S.: Different objective functions in fuzzy c-means algorithms and kernel-based clustering. Int. J. Fuzzy Syst. 13(2), 89–97 (2011)

    MathSciNet  Google Scholar 

  5. Shieh, H.-L.: A hybrid fuzzy clustering method with a robust validity index. Int. J. Fuzzy Syst. 16(1), 39 (2014)

    MathSciNet  Google Scholar 

  6. Streichert, F.: Introduction to evolutionary algorithms. In: Proceedings of Frankfurt Math Finance Workshop, Frankfurt, Hesse, April 2002

  7. Dolphins-world: http://www.dolphins-world.com/how-do-dolphins-catch-their-food/ (2014)

  8. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 265–323 (1999)

    Article  Google Scholar 

  9. Chowdary, N.S., Prasanna, D.S., Sudhakar, P.: Evaluating and analyzing clusters in data mining using different algorithms. Int. J. Comput. Sci. Mobile Comput. 3(2), 86–99 (2014)

    Google Scholar 

  10. Deza, E., Deza, M.M.: Encyclopedia of Distances, 2nd edn. Springer, Berlin (2009)

    Book  MATH  Google Scholar 

  11. Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine. http://archive.ics.uci.edu/ml (2013)

  12. Lou, X., Li, J., Liu, H.: Improved fuzzy c-means clustering algorithm based on cluster density. J. Comput. Inf. Syst. 2, 727–737 (2012)

    Google Scholar 

  13. Elena, M.: Fuzzy c-means clustering in Matlab. In: Proceedings of the 7th International Days of Statistics and Economics, Prague, pp. 905–914, September 2013

  14. Kumari, N., Sharma, B., Gaur, D.: Implementation of possibilistic fuzzy c-means clustering algorithm in Matlab. Int. J. Sci. Eng. Res. 3(11), 431–439 (2012)

    Google Scholar 

  15. Huffner, F.: Algorithm Design: Simplicity, GI-Dagstuhl Research Seminar 06362: Algorithm Engineering. Friedrich Schiller University, Jena (2006)

    Google Scholar 

  16. Wiswedel, B., Patterson, D.E., Berthold, M.R.: Interactive exploration of fuzzy clusters. In: Oliveira, J., Pedrycz, W. (eds.) Advances in Fuzzy Clustering and Its Applications, pp. 123–136. Wiley, Hoboken (2007)

    Chapter  Google Scholar 

  17. Shaikh, S., Rao, M., Mantha, S.S.: A new association rule mining based on frequent item set. Comput. Sci. Inf. Technol. 1, 81–95 (2011)

    Google Scholar 

  18. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 1st edn. Morgan Kaufmann Publishers, San Francisco (2001)

    MATH  Google Scholar 

  19. Thyroid stimulating hormone: http://www.webmd.com/women/thyroid-stimulating-hormone-tsh?page=2

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Acknowledgments

This work was supported in part by the Ministry of Science and Technology, Taiwan under Grants MOST103-2221-E-027-076-, MOST103-2221-E-027-122-MY2, and in part by the joint project between the National Taipei University of Technology and Mackay Memorial Hospital under Grants NTUT-MMH-103-01 and NTUT-MMH-104-03.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan

    Cheng-Ming Huang, Yusra Ghafoor & Yo-Ping Huang

  2. Department of Psychiatry, Mackay Memorial Hospital, Taipei, 10449, Taiwan

    Shen-Ing Liu

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  1. Cheng-Ming Huang
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  2. Yusra Ghafoor
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  3. Yo-Ping Huang
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  4. Shen-Ing Liu
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Correspondence to Yo-Ping Huang.

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Huang, CM., Ghafoor, Y., Huang, YP. et al. A Dolphin Herding Inspired Fuzzy Data Clustering Model and Its Applications. Int. J. Fuzzy Syst. 18, 299–311 (2016). https://doi.org/10.1007/s40815-015-0093-5

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  • DOI: https://doi.org/10.1007/s40815-015-0093-5

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