Stock Market Prediction Using Clustering with Meta-Heuristic Approaches

Abstract

Various examinations are performed to predict the stock values, yet not many points at assessing the predictability of the direction of stock index movement. Stock market prediction with data mining method is a standout amongst the most paramount issues to be researched and it is one of the interesting issues of stock market research over several decades. The approach of advanced data mining tools and refined database innovations has empowered specialists to handle the immense measure of data created by the dynamic stock market. Data mining strategies have been utilized to reveal hidden patterns and predict future patterns and practices in financial markets to help financial investors make qualitative choice. In this paper, the consistency of stock index movement of the well-known Indian Stock Market indices NSE-NIFTY are examined with the assistance of famous data mining strategies known as Clustering. Clustering is the methodology of grouping the alike indices into clusters. It likewise audits three of the meta-heuristics clustering algorithms: PSO-K-Means, Bat Algorithm, and firefly Algorithm. These strategies are implemented and tested against a Brain Tumor gene interpretation Dataset.  The performance of the aforementioned procedures is compared based on  "integrity of clustering" assessment measures. The investigation is used to the NSE-NIFTY and BSE-NIFTY for the period from January 2011 to April 2014.

 

Keywords

• Data Mining
• Clustering
• PSO
• BAT Algorithm
• Firefly Algorithm
• Prediction
• Stock Index Movement.

References

1. Dattolo A, Eynard D, Mazzola L, “An Integrating Approach To Discover Tag Semantics”, In Proceedings of the 2011 ACM Symposium on Applied Computing, March 21-24, TaiChung, Taiwan (2011).
2. Dhanalakshmi K, Inbarani HH , “Fuzzy Soft Rough K-Means Clustering Approach For Gene Expression Data”, Int. J. of Scientific Engineering and Research 3(10):1-7, (2012).
3. Esmin A.A, Coelho R.A, Matwin S A, “review on particle swarm optimization algorithm and its variants to clustering high-dimensional data”, Artificial Intelligence Review, 1–23, (2013).
4. Hammouda K A,” Comparative Study of Data Clustering Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada, (2006). Technical Report,
5. Hassanzadeh, T., Meybodi, M. R. “A New Hybrid Approach for Data Clustering Using Firefly Algorithm International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 007 – 011, (2012). 16thIEEECSI
6. Jain A.K, Murty M.N, and Flyn P.J., “Data Clustering: A Review”. ACM Computing Surveys 31(3):264-323, (1999).
7. Kumar SS, Inbarani HH, “Web 2.0 social bookmark selection for tag clustering”, In: Periyar University, (PRIME) Pattern Recognition, Informatics and Medical Engineering (PRIME), Salem, 22-23 Feb 2013, 510- 516, IEEE, (2013a).
8. Kumar SS, Inbarani HH, “Analysis of mixed C- means clustering approach for brain tumour gene expression data”. Int. J. of Data Analysis Techniques and Strategies, 5(2): 214 – 228, (2013b).

9. Kuo R.J, Wang M.J, Huang T.W, “An application of particle swarm optimization algorithm to clustering analysis”. Soft Computing 15(3):533–542, (2011).
10. Martens D, Baesens B, Fawcett T.. “Editorial survey: swarm intelligence for data mining”. Machine Learning 82(1):1–42,(2011).
11. Monica Sood and Shilpi Bansal, “K-Medoids Clustering Technique using Bat Algorithm”, International Journal of Applied Information Systems (IJAIS), 5(8):20-22, (2013).
12. Neshat M, Yazdi SF, Yazdani D and Sargolzaei M, “A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering”. J. of Computer Science 8(2):188-194, (2012).
13. Taher N, Babak A, “An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis”, Appl Soft Comput 10(1):183–197, (2010).
14. Xin-She Yang, “A new metaheuristic Bat inspired Algorithm”. Studies in Computational Intelligence, Springer, (2010) .
15. Yau K.L, Tsang P.W.M, Leung C.S “PSO-based K- means clustering with enhanced cluster matching for gene expression data”, Neural Computing and Application 22(7-8): 1349–1355, (2013).
16. Lei, Y., He, Z., Zi, Y., “Application of an intelligent classification method to mechanical fault diagnosis”, Expert Systems with Applications 36: 9941–9948 (2009).
17. Toutountzakis, T., Tan, C. K., Mba, D., “Application of acoustic emission to seeded gear fault detection” NDT & E International, 38(1): 27– 36 (2005).
18. Liu, B., Ling, S. F., Gribonval, R., “Bearing failure detection using matching pursuit”. NDT&E International, 35: 255–262 (2002).
19. Yang B. S., Lim D. S., Tan, A. C. C., “VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table” Expert Systems with Application, 28(4): 735– 742 (2005).
20. Peng, Z. K., Chu, F. L., “Application of wavelet transform in machine condition monitoring and fault diagnostics: Mechanical Systems and Signal Processing, 17: 199–221, 2003. with bibliography”.
21. Huang, N. E., Shen, Z., Long, S. R., “The Empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”. Proceedings of the Royal Society of London, 454: 903–995. (1998).
22. Lee, S. K., White, P. R., “Higher-order time- frequency analysis and its application to fault detection in rotating machinery”. Mechanical Systems and Signal Processing, 11(4): 637–650 (1997).
23. Younus, A. MD., Yang, B., “Intelligent fault diagnosis of rotating machinery using infrared thermal image”, Expert Systems with Applications 39: 2082–2091 (2012).
24. Kad, R. S., “IR thermography is a Condition Monitor Technique in industry”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(3): 988-993 (2013).
25. Zhang P., Lu B., Habetler T.G., “Active Stator Winding Thermal Protection for Ac Motors”, Proceedings of IEEE IAS Pulp and Paper Industry Conference, Alabama, USA, (2009)
26. Nandi, S., Toliyat, H. A. and Li, X., “Condition monitoring and fault diagnosis of electric motors—a review”, IEEE Trans. on energy conversion, 20(4): 719-129, (2005).
27. Carderock Division Naval Surface Warfare Center, “Handbook of Reliability Prediction Procedures for Mechanical Equipment” (2010).
28. Barreira, E., de Freitas, V.P., Delgado, J.M.P.Q. and Ramos, N.M.M., “Thermography Applications in the Study of Buildings Hygrothermal Behaviour, Infrared Thermography”, Dr. Raghu V Prakash (Ed.), ISBN: 978-953-51-0242-7 (2012).
29. Stipetic, S., Kovacic, M., Hanic, Z., Vrazic, M., “Measurement of Excitation Winding Temperature on Synchronous Generator in Rotation Using Infrared Thermography”, IEEE Transactions on Industrial Electronics, 59 (5): 2288-2298 (2012).
30. Fantidis J. G., Karakoulidis K., Lazidis G., Potolias C., Bandekas D. V., “The study of the thermal profile of a three-phase motor under different conditions”, ARPN Journal of Engineering and Applied Sciences, 8 (11): 892 – 899 (2013).