Fast time sequence data mining algorithm based on grey system theory
/ 0
- Details
- Category: Information technologies, systems analysis and administration
- Last Updated on 19 January 2017
- Published on 19 January 2017
- Hits: 4143
Authors:
Hongyi Cao, Xi’an Medical University, Xi’an 710021, Shaanxi, China
Junhui Yang, Xi’an Medical University, Xi’an 710021, Shaanxi, China
Li Wang, Xi’an Medical University, Xi’an 710021, Shaanxi, China
Abstract:
Purpose. With the development of the big data technology, the time sequence data mining has become a hot spot that attracts the attention of the public. Based on the correlation and cooperativity of the time sequence data, we propose the fast time sequence data mining model based on the grey system theory.
Methodology. The correlation determination method that is based on the features of the relevant coefficient of the time shift sequence is obtained. As a result, a kind of fast time sequence data mining model based on the grey system theory is proposed.
Findings. The correlation determination methodology proposed in this paper is more effective than the Pearson linear correlation coefficient, Spearman rank correlation coefficient, Kendall rank correlation coefficient and Granger causality test.
Originality. In this paper, the double sequence fast correlation determination method and curve alignment method are provided. So far, we have not found other literature on the related research.
Practical value. The research results can provide theoretical basis for the determination of the correlation of regression analysis and the time alignment.
References/Список літератури
1. Yin, M.S., 2013. Fifteen years of grey system theory research: a historical review and bibliometric analysis. Expert systems with Applications, Vol. 40, No. 7, pp. 2767–2775.
2. Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A. and Joseph M. Hellerstein, 2012. Distributed GraphLab: a framework for machine learning and data mining in the cloud’ Proceedings of the VLDB Endowment, Vol. 5, No. 8, pp. 716–727.
3. Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hoče var, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M. and Zupan, B., 2013. Orange: data mining toolbox in Python. The Journal of Machine Learning Research, Vol. 14, No. 1, pp. 2349–2353.
4. Nguyen, P.H., Sheu, T.W., Nguyen, P.T., et al., 2014. Taylor Approximation Method in Grey System Theory and Its Application to Predict the Number of Foreign Students Studying in Taiwan, International Journal of Innovation and Scientific Research, Vol. 10, No. 2, pp. 409–420.
5. Romero, C. and Ventura, S., 2013. Data mining in education, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 3, No. 1, pp. 12–27.
6. Tserng, H.P., Ngo, T.L. and Chen, P.C., 2015. A Grey System Theory Based Default Prediction Model for Construction Firms. Computer Aided Civil and Infrastructure Engineering, Vol. 30, No. 2, pp. 120–134.
7. Wei, M.C., 2014. The Influence Factor Analysis for Sexual Harassment on Campus in Taiwan via Grey System Theory. Journal of Grey System, Vol. 17, No. 4, pp. 207–213.
8. Ghodrati Amiri, G., Zare Hosseinzadeh, A. and Jafarian Abyaneh, M., 2016. A new two-stage method for damage identification in linear-shaped structures via Grey System Theory and optimization algorithm, Journal of Rehabilitation in Civil Engineering, Vol. 3, No. 2, pp. 36–50.
9. Raju, P.S., Bai, D.V.R. and Chaitanya, G.K., 2014. Data mining: Techniques for enhancing customer relationship management in banking and retail industries. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 1, pp. 2650–2657.
10. Liao, S.H., Chu, P.H. and Hsiao, P.Y., 2012. Data mining techniques and applications–A decade review from 2000 to 2011. Expert Systems with Applications, Vol. 39, No. 12, pp. 11303–11311.
 06_2016_Hongyi
|
|
 2017-01-19  496.51 KB  894 |
|
Archive