Title :
The Mining Method of the Road Traffic Illegal Data Based on Rough Sets and Association Rules
Author :
Cheng, Wei ; Ji, Xiaofeng ; Han, Chunhua ; Xi, Jianfeng
Author_Institution :
ITS Center, Kunming Univ. of Sci. & Technol., Kunming, China
Abstract :
Based on the daily operation data of the urban road traffic management system, this paper analysis the demand of data mining of the traffic violations, pre-processes the data to data sets by the detection methods of proximity-based outlier. According to the characteristics of data traffic offense, combining the advantages of rough sets and association rules data mining, proposed two methods based on the joint data mining method. Finally, a city in the year 2008 road traffic management data, for example, using the text method, regularity of the traffic offense causes were analyzed, indicating that the method is effective.
Keywords :
data mining; road traffic; rough set theory; traffic engineering computing; association rules; data mining method; data sets; data traffic offense; proximity-based outlier; road traffic illegal data; rough sets; traffic violations; urban road traffic management system; Association rules; Data analysis; Data mining; Information analysis; Management training; Roads; Rough sets; Technology management; Traffic control; Vehicle driving; Anomaly detection theory; Association rules; Data mining; Rrough sets; Traffic violate;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
DOI :
10.1109/ICICTA.2010.803
