A Mobile Data Minging Algorithm Based on Discrete Fourier Transform by Genetic Algorithm

Shu Ang Wei; Li Gao; Zhi Ye Sun; Shi Jue Zheng

doi:10.4028/www.scientific.net/AMR.108-111.1452

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 108-111A Mobile Data Minging Algorithm Based on Discrete...

A Mobile Data Minging Algorithm Based on Discrete Fourier Transform by Genetic Algorithm

Abstract:

In the mobile environment, considering resource constraint, the frequent disconnect, synchronous data flow, the cost of communication, mobility and so on, combined with discrete Fourier transform (Discrete Fourier Transform, DFT) algorithm to facilitate time-domain and frequency domain conversion advantages as well as the genetic algorithm’s (Genetic Algorithm, GA) good global search capability ,this paper proposes a mobile data mining model which is based on the combination of Discrete Fourier Transform and Genetic Algorithm (DFTGA).

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Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

1452-1457

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.108-111.1452

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Online since:

May 2010

Authors:

Shu Ang Wei, Li Gao, Zhi Ye Sun, Shi Jue Zheng

Keywords:

DFTGA, Discrete Fourier Transform, Genetic Algorithm (GA), Mobile Data Mining

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References

[1] Agrawal, R., Imielinksi, T., & Swami, A. (1993a). Mining association rules between sets of items in large database. In The ACM SIGMOD conference, Washington, DC, USA.

DOI: 10.1145/170035.170072

Google Scholar

[2] Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules. In The international conference on very large data bases (pp.487-499).

Google Scholar

[3] Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In The 7th internationalconference on data engineering (pp.3-14).

Google Scholar

[4] Agrawal, R., Srikant, R., & Vu, Q. (1997). Mining association rules with item constraints. In The 3rd international conference on knowledge discovery in databases and data mining, Newport Beach, California.

Google Scholar

[5] Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In The ACM SIGMOD conference, Tucson, Arizona, USA (pp.255-264).

DOI: 10.1145/253262.253325

Google Scholar

[6] Frawley, W. J., Shapiro, G. P., & Matheus, C. J. (1991). Knowledge discovery in databases: An overview. In The AAAI workshop on knowledge discovery in databases (pp.1-17).

Google Scholar

[7] Han, J., & Fu, Y. (1995). Discovery of multiple-level association rules from large database. In The 21st international conference on very large data bases, Zurich, Switzerland (pp.420-431).

Google Scholar

[8] Han, J., Pei, J., & Yin, Y. (2006). Mining frequent patterns without candidate generation. In ACM SIGMOD conference (pp.1-12).

DOI: 10.1145/335191.335372

Google Scholar

[9] Li, D., & Deogun, J. S. (2005). Discovering partial periodic sequential association rules with time lag in multiple sequences for prediction. Lecture Notes in Computer Sciences, 3488, 332-341.

DOI: 10.1007/11425274_35

Google Scholar

[10] Li, Y., Ning, P., Wang, X. S., & Jajodia, S. (2003). Discovering calendar-based temporal association rules. Data and Knowledge Engineering, 193-218.

DOI: 10.1016/s0169-023x(02)00135-0

Google Scholar

[11] Mannila, H., Toivonen, H., & Verkamo, I.A. (1999). Efficient algorithm for discovering association rules. In The AAAI workshop knowledge discovery in databases (pp.181-192).

Google Scholar

[12] Ozden, B., Ramaswamy, S., & Silberschatz, A. (1998). Cyclic association rules. In The 14th international conference on data engineering, Orlando, Florida, USA (pp.12-21).

DOI: 10.1109/icde.1998.655804

Google Scholar