Incremental Mining for Temporal Association Rules for Crime Pattern Discoveries

In recent years, the concept of temporal association rule (TAR) has been introduced in order to solve the problem on handling time series by including time expressions into association rules. In real life situations, temporal databases are often appended or updated. Rescanning the complete database every time is impractical while existing incremental mining techniques cannot deal with temporal association rules. In this paper, we propose an incremental algorithm for maintaining temporal association rules with numerical attributes by using the negative border method. The new algorithm has been implemented to support the discoveries of crime patterns in a district of Hong Kong. We have also experimented with another real life database of courier records of a shipping company. The preliminary results show a significant improvement over rerunning TAR algorithm..

• Type: Conference
• Year: 2007
• Where: ADC
• Authors: Vincent T. Y. Ng, Stephen Chi-fai Chan, Derek Lau, Cheung Man Ying
• Keywords: ADC 2007, Database, In Real Life, Real Life, Temporal Association Rules