Title :
A study on time based association rule mining on spatial-temporal data for intelligent transportation applications
Author :
Lanka, Swathi ; Jena, S.K.
Author_Institution :
CSE Dept., NIT, Rourkela, India
Abstract :
Discovery of association rules is one of the very important tasks in data mining. So far Conventional Association Rule Mining (CARM) has proven its importance in medical, biology and business fields. As it is unable to extract time based association rules, it substantiated to unsuitable for intelligent transportation applications. The CARM extended to spatiotemporal processes, generating time based Association Rule Mining (TARM) which is used to extract time based association rules. TARM found suitable for intelligent transportation applications such as traffic prediction, travel time estimation, congestion prediction etc. We have defined TARM and time related class association rules, based on spatio-temporal database. This paper presents an analysis on different data mining algorithms, soft and evolution computation techniques which are focused on extracting transactional and time based association rules.
Keywords :
data mining; intelligent transportation systems; road traffic; TARM; congestion prediction; data mining; data mining algorithms; intelligent transportation applications; spatiotemporal database; spatiotemporal processes; time based association rule extraction; time based association rule mining; time based association rules; time related class association rules; traffic prediction; transactional rule extraction; travel time estimation; Algorithm design and analysis; Association rules; Databases; Magnetic sensors; Roads; formatting; insert; style; styling;
Conference_Titel :
Networks & Soft Computing (ICNSC), 2014 First International Conference on
Conference_Location :
Guntur
Print_ISBN :
978-1-4799-3485-0
DOI :
10.1109/CNSC.2014.6906690