A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets

Salam Al-Augby; Sebastian Majewski; Agnieszka Majewska; Kesra Nermend

doi:10.1515/foli-2015-0001

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A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets

Folia Oeconomica Stetinensia

Volume 14 (2014): Issue 2 (December 2014)

By: Salam Al-Augby, Sebastian Majewski, Agnieszka Majewska and Kesra Nermend

Open Access

|Jun 2015

Abstract

The main goal of this article is to compare data-mining clustering methods (k-means and fuzzy c-means) based on a sample of banking and energy companies on the Gulf Cooperation Council (GCC) stock markets. We examined these companies for a pattern that reflected the effect of news on the bank sector’s stocks throughout October, November, and December 2012. Correlation coefficients and t-statistics for the good news indicator (GNI) and the bad news indicator (BNI) and financial factors, such as PER, PBV, DY and rate of return, were used as diagnostic variables for the clustering methods.

References

Alves, A., Camacho, R. & Oliveira, E. (2004). Inductive Logic Programming for Data Mining in Economics. The 2nd International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management. Pisa: University of Porto.
Search in Google Scholar
Anderberg, M.R. (1973). Cluster Analysis for Applications. New York: Academic Press.
Search in Google Scholar
Andreassen, P.B. (1987). On the social psychology of the stock market. Aggreagat attributional effects and the regressivness of prediction. Journal of Personality and Socioal Psychology, 53 (3), 490–496.
Search in Google Scholar
Bezdek, J.C. (1980). A convergence theorem for the fuzzy ISODATA clustering Algorithms. IEEE Trans. Pattern Anal. Machine Intell, 2, 1–8.10.1109/TPAMI.1980.4766964
Search in Google Scholar
Bezdek, J.C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.10.1007/978-1-4757-0450-1
Search in Google Scholar
Bezdek, J.C., Ehrlich, R. & Full, W. (1984). FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences, 10, 191–203.10.1016/0098-3004(84)90020-7
Search in Google Scholar
Błażewicz, J., Kubiak, W., Morzy, T. & Rusinkiewicz, M. (2003). Handbook on Data Management in Information Systems. Springer-Verlag.10.1007/978-3-540-24742-5
Search in Google Scholar
Bose, I. & Mahapatra, R.K. (2001). Business data mining – a machine learning perspective. Information & Management, 39, 211–225.10.1016/S0378-7206(01)00091-X
Search in Google Scholar
Business (10, 11, 12.2012), www.reuters.com/finance/economy.
Search in Google Scholar
Bussiness and Technology (10, 11, 12.2012). From AL ARABIA NEWS: http://english.alarabiya.net/index.
Search in Google Scholar
Calinski, R.H. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3, 1–27.
Search in Google Scholar
Cao, L., Yu, P.S., Zhang, C. & Zhang, H. (2009). Data Mining for Business Applications. New York: Springer.10.1007/978-0-387-79420-4
Search in Google Scholar
Carretta, A., Farina, V., Martelli, D., Fiordelisi, F. & Schwizer, P. (2011). The impact of corporate governance press news on stock market returns. European financial management, 17 (1), 100–119.10.1111/j.1468-036X.2010.00548.x
Search in Google Scholar
Chiang, M.M.-T. & Mirkin, B. (2010). Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads. Journal of Classification, 27, 3–40.10.1007/s00357-010-9049-5
Search in Google Scholar
Clustering (2012, June 8). From Computer Science 831: Knowledge Discovery in Databases: www2.cs.uregina.ca/~dbd/cs831/notes/clustering/clustering.html (7.03.2013).
Search in Google Scholar
Deza, E. & Deza, M.M. (2009). Encyclopedia of Distances. Berlin, Heidelberg: Springer-Verlag.10.1007/978-3-642-00234-2
Search in Google Scholar
Dunham, M.H. (2002). Data Mining: Introductory and Advanced Topics. New York: Prentice Hall.
Search in Google Scholar
Elavarasi, S.A., Akilandeswari, J. & Sathiyabhama, B. (2011). A Survey on Partition Clustering Agorithms. International Journal of Enterprise Computing and Business Systems, 1, 1–14.
Search in Google Scholar
Elmasri, R. & Navathe, S.B. (2011). Fundamentals of database systems. Boston, MA: Addison-Wesley.
Search in Google Scholar
Fairfield, P.M. (1994). P/E, P/B and the Present Value of Future Dividends. Financial Analysts’ Journal, 23–31.10.2469/faj.v50.n4.23
Search in Google Scholar
Field, A. (2009). Discovering Statistics Using SPSS. New Delhi: Sage Publications.
Search in Google Scholar
Fridson, M.S. (2011). Financial Statement Analysis. A Practitioner’s Guide. New Jersey: John Wiley & Sons.
Search in Google Scholar
Gasch, A.P., & Eisen, M.B. (2002). Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology, 3, 1–22.10.1186/gb-2002-3-11-research0059
Search in Google Scholar
Ghosh, J. & Liu, A. (2009). K-Means. In: W. Xindong, V. Kumar, The top ten algoritms in Data Mining (pp. 21–36). Boca Raton, Florida: Taylor & Francis Group.
Search in Google Scholar
Gorsevski, P.V., Gessler, P.E. & Jankowski, P. (2003). Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical System, 223–251.10.1007/s10109-003-0113-0
Search in Google Scholar
Hammoudeh, S. & Choi, K. (2006). Behavior of GCC stock markets and impacts of US oil and financial markets. Research in International Business and Finance, 20, 22–44.10.1016/j.ribaf.2005.05.008
Search in Google Scholar
Han, J. & Kamber, M. (2006). Data Mining:Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers.
Search in Google Scholar
Hertog, S. (November 2012). Financial markets in GCC countries: recent crises and structural weaknesses. Norwegian Peacebuilding Resource Centre.
Search in Google Scholar
Huang, Z. (1997). A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. Research Issues on Data Mining and Knowledge Discovery. Cite Seer, 1–8.
Search in Google Scholar
Huang, Z. & Ng, M.K. (1999). A Fuzzy K-Modes Algorithm for Clustering Categorical Data. IEEE Transactions on Fuzzt Systems, 7 (4), 446–452.10.1109/91.784206
Search in Google Scholar
Investmens Policy. (2013). Calgary.
Search in Google Scholar
Jain, A.K. & Dubes, R.C. (1988). Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice Hall.
Search in Google Scholar
KAMCO (10, 11, 12.2012). Research Reports, www.kamconline.com (01.2013).
Search in Google Scholar
Kudyba, S. (2004). Managing Data Mining, Advice from Experts. USA: IT Solutions Series, Idea Group.10.4018/978-1-59140-243-5
Search in Google Scholar
Kumar, P. & Wasan, S.K. (2010). Comparative Analysis of k-mean Based Algorithms. International Journal of Computer Science and Network Security, 10 (4), 314–318.
Search in Google Scholar
Kumar, V., Joshi, M.V., Han, E.-H.S., Tan, P.-N. & Steinbach, M. (2003). High performance data mining. High Performance Computing for Computational Science – VECPAR 2002, 111–125.10.1007/3-540-36569-9_8
Search in Google Scholar
Larose, D.T. (2005). Discovering Knowledge in Data (An Introduction to Data Mining). Hoboken, NJ: John Wiley & Sons.
Search in Google Scholar
Levinson, M. (2006). Guide to Financial Markets (pp. 145–146). London: The Economist (Profile Books).
Search in Google Scholar
Li, M.J., Ng, M.K., Cheung, Y.-M, & Huang, J.Z. (2008). Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters. IEEE Transactions on Knowledge and Data Engineering, 20 (11), 1519–1534.10.1109/TKDE.2008.88
Search in Google Scholar
Lo, A.W., & MacKinlay, A.C. (1988). Stock Market Prices Do not Follow Random Walks: Evidence from a Simple Specification Test. The Review of Financial Studies, 41–66.10.1093/rfs/1.1.41
Search in Google Scholar
Luo, F., Wu, J. & Yan, K. (2010). A Novel Nonlinear Combination Model Based on Support Vector Machine for Stock Market Prediction. 8th World Congress on Intelligent Control and Automation (p. 1). Jinan, China: IEEE.
Search in Google Scholar
Madhulatha, T.S. (2012). An Overview On Clustering Methods. IOSR Journal of Engineering, 2 (4), 719–725.10.9790/3021-0204719725
Search in Google Scholar
Majewski, S. (2009). The media and the prices creation in Poland. International Journal of Management Cases, 11 (1), 70–77.10.5848/APBJ.2009.00008
Search in Google Scholar
Majewski, S., Nermend, K. & Al-augby, S. (2012). Media and Price Creation in Abu Dhabi Security Exchange. Sientific Papers of the Polish Information Processing Society Sientific Council, University of Szczecin, 81–93.
Search in Google Scholar
Marghescu, D., Sarlin, P. & Liu, S. (2010). Early-Warning Analysis for Currency Crises in Emerging Markets: A Revisit With Fuzzy Clustering. Intellegent Systems in Accounting, Finance and Management, 17, 143–165.10.1002/isaf.317
Search in Google Scholar
Mathuriya, N. & Bansal, A. (2012). Comparison of K-means and means and Back propagation Data Mining Algorithms. International Journal of Computer Technology and Electronics Engineering, 151–155.
Search in Google Scholar
McBratney, A.B. & De Gruijter, J.J. (1992). A Continuum Approach to Soil Classification by Modified Fuzzy K-means with Extragrades. Journal of Soil Science, 43, 159–175.10.1111/j.1365-2389.1992.tb00127.x
Search in Google Scholar
Mhmoud, A.S. & Ali, S.O. (2013). Application of Principal Component Method and k-me ans clustering algorithm for Khartoum stock Market. Nature and Science, 108–112.
Search in Google Scholar
Mirkin, B.G. (1996). Mathematical classification and clustering. Dordrecht: Kluwer Academic Publishing.10.1007/978-1-4613-0457-9
Search in Google Scholar
Mitchell, M.L. & Mulherin, J.H. (1994). The impact of public information on the stock market. The Journal of Finance, 49 (3), 923–950.10.1111/j.1540-6261.1994.tb00083.x
Search in Google Scholar
Mooi, E. & Sarstedt, M. (2011). A Concise Guide to Market Research The Process, Data, and Methods Using IBM SPSS Statistics. Berlin: Springer-Verlag.
Search in Google Scholar
Nanda, S.R., Mahanty, B. & Tiwari, M.K. (2010). Clustering Indian stock market data for portfolio management. Expert Systems with Applications 37, 8793–8798.10.1016/j.eswa.2010.06.026
Search in Google Scholar
Nikam, V., Kadam, V.J. & Meshram, B.B. (2011). Image Compression Using Partitioning Around Medoids Clustering Algorithm. International Journal of Computer Science Issues, 8, 6 (1), 399–401.
Search in Google Scholar
Ramamurthy, B. & Chandran, K.R. (2011). CBMIR: Shape-BasedImage Retrieval Using Canny Edge Detection and K-Means Clustering Algorithms for Medical Images. International Journal of Engineering Science and Technology, 3, 1870–1877.
Search in Google Scholar
Ruspini, E.R. (1969). A new approach to clustering. Inform. Control, 19, 22–32.10.1016/S0019-9958(69)90591-9
Search in Google Scholar
Santosh, K.C. & Nattee, C. (2009). A Comperhensive Survey on On-line Handwriting Recgnition Technology and Its Real Application to The Nepalese NaturalL Handwriting. Kathmandu University Journal of Science, Engineering and Technology, 5 (1), 31–55.
Search in Google Scholar
Setty, D.V., Rangaswamy, T.M. & Subramanya, K.N. (2010). A Review on Data Mining Applications to the Performance of Stock Marketing. International Journal of Computer Applications, 1 (3), 24–34.10.5120/88-187
Search in Google Scholar
Shiller, R.J. (2001). Irrational Exuberance. New York: Brodway Books, p. 95.
Search in Google Scholar
Shrestha, D. (2009). Text Mining with Lucene and Hadoop: Document Clustering With Feature Extraction. Research Degree Thesis. Wakhok University.
Search in Google Scholar
Simpson, J. (2008). Financial Integration In The GCC Stock Markets: Evidence From The Early 2000s Development Phase. Journal of Economic Cooperation, 1–28.
Search in Google Scholar
Singh, K., Malik, D. & Sharma, N. (2011). Evolving limitations in K-means algorithm in data mining and their removal. International Journal of Computational Engineering & Management, 12, 105–109.
Search in Google Scholar
StatSoft (2013). StatSoft Electronic Statistics Textbook. From Introduction to ANOVA/MANOVA: www.thefullwiki.org/Analysis_of_variance.
Search in Google Scholar
Sugar, C.A. & James, G M. (2003). Finding the number of clusters in a data set :An information theoretic approach. Journal of the American Statistical Association, 98 (463), 750–763.10.1198/016214503000000666
Search in Google Scholar
Tan, P.-N., Steinbach, M. & Kumar, V. (2006). Introduction to Data Mining. Pearson Addison Wesley.
Search in Google Scholar
Thompson, B. (2002). “Statistical,” “Practical,” and “Clinical”: How Many Kinds of Significance Do Counselors Need to Consider? Journal of Counseling & Development, 80, 64–71.
Search in Google Scholar
Triantaphyllou, E. (2010). Data Mining and Knowledge Discovery Via Logic-Based Methods. New York: Springer.10.1007/978-1-4419-1630-3
Search in Google Scholar
Vassilios, C., Adrian, G.B. & Ioannis, P. (1999). Multimodal Decision-Level Fusion for Person Authentication. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 674–680.
Search in Google Scholar
Vimal, A., Valluri, S.R. & Karlapalem, K. (2008). International Conference on Management of Data COMAD 2008. Mumbai: Computer Society of India.
Search in Google Scholar
Wei, Y. (2005, May). Approximation To K-means Clustering. Hamilton, Ontario, Canada: McMaster University.
Search in Google Scholar
Witten, I.H. & Eibe, F. (2005). Data Mining Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann Publishers is an imprint of Elsevier.
Search in Google Scholar
Xu, R. & II, D.W. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neura Networks, 16 (3), 645–678.10.1109/TNN.2005.845141
Search in Google Scholar
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8 (3), 338–353.10.1016/S0019-9958(65)90241-X
Search in Google Scholar
Zaki, M.J. & Jr., W.M. (2013). Data Mining and Analysis:Fundamental Concepts and Algorithms. Draft copy: Cambridge University Press.
Search in Google Scholar
Zielonka, P. (2000). Biased Judgement on What Moves Stock Prices. Warsaw: Institute of Philosophy and Sociology Polish Academy of Sciences.
Search in Google Scholar

Articles in this issue

DOI: https://doi.org/10.1515/foli-2015-0001 | Journal eISSN: 1898-0198 | Journal ISSN: 1730-4237

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Language: English

Page range: 19 - 36

Submitted on: Feb 3, 2014

Accepted on: Oct 24, 2014

Published on: Jun 3, 2015

Published by: University of Szczecin

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

news,

k-means,

GCC,

stock market,

fuzzy c-means

Related subjects:

Business and economics,

Political economics,

Political economics, other

© 2015 Salam Al-Augby, Sebastian Majewski, Agnieszka Majewska, Kesra Nermend, published by University of Szczecin
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 14 (2014): Issue 2 (December 2014)