A study on time based association rule mining on spatial-temporal data for intelligent transportation applications

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.