Incremental mining for temporal association rules for crime pattern discoveries

Vincent To Yee Ng, Stephen Chan, Derek Lau, Cheung Man Ying

Research output: Journal article publicationConference articleAcademic researchpeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)123-132
Number of pages10
JournalConferences in Research and Practice in Information Technology Series
Publication statusPublished - 1 Dec 2007
Event18th Australasian Database Conference, ADC 2007 - Ballarat, VIC, Australia
Duration: 30 Jan 20072 Feb 2007


  • Crime analysis
  • Incremental mining
  • Temporal association rules

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Software


Dive into the research topics of 'Incremental mining for temporal association rules for crime pattern discoveries'. Together they form a unique fingerprint.

Cite this