Abstract
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 language | English |
|---|---|
| Pages (from-to) | 123-132 |
| Number of pages | 10 |
| Journal | Conferences in Research and Practice in Information Technology Series |
| Volume | 63 |
| Publication status | Published - 1 Dec 2007 |
| Event | 18th Australasian Database Conference, ADC 2007 - Ballarat, VIC, Australia Duration: 30 Jan 2007 → 2 Feb 2007 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- Crime analysis
- Incremental mining
- Temporal association rules
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Information Systems
- Software
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