A Recommendation System of Highway ETC Card Based on Decision Tree Theory

Abstract:

With the promotion of social information construction and the rapid update and replacement of large capacity storage equipment, the amount of data from every field grows exponentially. Reportedly, the amount of the data accumulated by Shandong Hi-speed Group is very large. These data can satisfy us some daily usefulness, such as query, retrieval, statistics, statements etc. But what is more important is that how can we discover some useful information from the information ocean. This information can be used in real life such as auxiliary decision. This paper is proposed in this historical background. Data mining is a powerful tool for acquiring knowledge from massive data. Some methods of data mining, such as decision tree, support vector machine, Bayesian decision theory, artificial neural network, k-nearest neighbor, association rule mining etc, are commonly used. In this paper, we design a recommendation system of highway ETC card by using the theory of decision tree. The recommendation system can predict whether a car owner is a potential ETC customer or not through the analysis of the vehicle information. Experiments proved that the accuracy rate of the recommendation system is larger than 90%, so it can provide effective information for the extension of China's ETC card.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 2411-2415
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.2411
• Online since: September 2014
• Authors: Yu Ling Ma*
• Keywords: ANN, Data Mining (DM), Decision Tree, Electronic Toll Collection System, Information Gain, K-Nearest Neighbor (KNN), Recommendation System of ETC Card, SVM

References

[1] Hang Li，Statistical learning method [M], Tsinghua University press . 55~56.

[2] Jiawei Han, and Micheline Kamber: Data Mining Concepts and Techniques[C], Morgan Kaufmann Publishers, (2000).

[3] MehtaM & Agrawal Retal. SLIQ: A fasts calable classifier for data mining InProc[C]. 1996 Int. Conf. ExtendingDataBase echnology. Avignon, France. Mar. (1996).

[4] GehrkeJetal. Rainforest: A framework for fast decision tree on struction of large datasets. InProc[C]. 1998Int. Conf. Very Large Data Bases. New York, USA. Aug. 1998: 416427.

[5] GehrkeJetal. BOAT: Optimistic decision tree construction[C] . In Proc. 1999ACM-SIGMOD Int. Conf. Managemet of Data. Philadelphia, USA, June 1999: 169~180.

[6] Zhi-Hua Zhou, Zhao-Qian Chen, Hybrid Decision Tree[C]，Knowledge-Based Systems, 2002, vol. 15, no. 8, pp.515-528.

[7] LIU, TING, YU, & ZHOU Spectrum of Variable-Random Trees[C].

[8] Quinlan JR. Induction of decision trees. Machine Learning[M], 1986, 1(1)81-106.