A Fault Diagnosis Method Based on Constrained Frequent Pattern Trees

Abstract:

Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. The running results show that it is feasible and valuable to apply this method to mining the association rule and the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm. Finally, the simulation results of knowledge acquisition for fault diagnosis also show the validity of CFP algorithm.

• Periodical: Applied Mechanics and Materials (Volume 39)
• Pages: 449-454
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.39.449
• Online since: November 2010
• Authors: Jiang Hui Cai, Wen Jun Meng, Zhi Mei Chen
• Keywords: Association Rule, Constrained Frequent Pattern Tree, Fault Diagnosis

References

[1] H. Mannila, H. Toivonen, and A. Verkamo: Proc. AAAI Workshop on Knowledge Discovery in Databases, (Seattle, USA, July, 1994). Vol. 1, p.181.

[2] H. Toivonen: Proc. the 22nd International Conference on Very Large Data Base(Bombay, India,. September, 1996). Vol. 115, p.28.

[3] R. Agrawal and R. Srikant: Proc. of the 20th Intern'l Conf. on Very Large Data Bases (Santiago, Chile, September 12-15, 1994). Vol. 102, p.134.

[4] M.J. Zaki, S. Parthasarathy, W. Li, and M. Ogihara: Proc. Of 3rd Int'l Conference on Knowledge Discovery&Data Mining(Newport, CA, August, 1997) . Vol. 12, pp.42-50.

[5] N. Alon and J. Spencer: The Probabilistic Method (second ed. ), (Wiley, USA, 2000).

[6] B. Chen, P. Haas, and P. Scheuermann: Proc. ACM-SIGKDD Conference on Knowledge Discovery and Data mining (Edmonton, Canada, July 23, 2002), Vol. 1, p.462.

[7] Agrawal R, Imielinski T and Swami A: Proc. ACM SIGMOD International Conference on Management of Data (Washington DC, USA, 1993). Vol. 22, p.207.

[8] J.W. Han, J. Pei, Y. W. Yin and R.Y. Mao: J. Data Mining and Knowledge Discovery, Vol. 8 (2004) No. 1, p.53.

[9] J. Pei, H. X . Wang and J. Liu , et al, IEEE Trans. knowledge and Data Engineering, Vol. 18(2006) No. 11, p.1467.

[10] A.L. Chen, C.J. Tang and H.C. Tao, et al: Journal of Software, Vol. 15(2004) No. 8, p.1198(In Chinese).

[11] Y.L. Wang: Control Eengineering, Vol. 11(2004) No. 5, p.406 (In Chinese).

[12] J. Gao S.J. Li and F. Qian: Congputer Engineering and Application, Vol. 43 (2007) No. 7, p.188 (In Chinese).