1. bookVolume 14 (2014): Issue 2 (December 2014)
Journal Details
First Published
06 May 2008
Publication timeframe
2 times per year
Open Access

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

Published Online: 03 Jun 2015
Volume & Issue: Volume 14 (2014) - Issue 2 (December 2014)
Page range: 19 - 36
Received: 03 Feb 2014
Accepted: 24 Oct 2014
Journal Details
First Published
06 May 2008
Publication timeframe
2 times per year

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.1980.4766964Search in Google Scholar

Bezdek, J.C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.10.1007/978-1-4757-0450-1Search 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)90020-7Search in Google Scholar

Błażewicz, J., Kubiak, W., Morzy, T. & Rusinkiewicz, M. (2003). Handbook on Data Management in Information Systems. Springer-Verlag.10.1007/978-3-540-24742-5Search 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-XSearch 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.10.1007/978-0-387-79420-4Search 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.xSearch 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/s00357-010-9049-5Search 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.10.1007/978-3-642-00234-2Search 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.n4.23Search 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-research0059Search 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/s10109-003-0113-0Search 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.2005.05.008Search 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), 446–452.10.1109/91.784206Search 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.10.4018/978-1-59140-243-5Search 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_8Search 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.2008.88Search 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/1.1.41Search 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), 719–725.10.9790/3021-0204719725Search 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.2009.00008Search 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.317Search 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.xSearch 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.10.1007/978-1-4613-0457-9Search 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.xSearch 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.2010.06.026Search 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)90591-9Search 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), 24–34.10.5120/88-187Search 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), 750–763.10.1198/016214503000000666Search 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.10.1007/978-1-4419-1630-3Search 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.2005.845141Search in Google Scholar

Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8 (3), 338–353.10.1016/S0019-9958(65)90241-XSearch 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

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