Publication: Structure-based rule selection framework for association rule mining of traffic accident data
Issued Date
2007-12-01
Resource Type
ISSN
16113349
03029743
03029743
Other identifier(s)
2-s2.0-38349033662
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Mahidol University
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SCOPUS
Bibliographic Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.4456 LNAI, (2007), 231-239
Suggested Citation
Rangsipan Marukatat Structure-based rule selection framework for association rule mining of traffic accident data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.4456 LNAI, (2007), 231-239. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/24382
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Title
Structure-based rule selection framework for association rule mining of traffic accident data
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Abstract
A rule selection framework is proposed which classifies, selects, and filters out association rules based on the analysis of the rule structures. It was applied to real traffic accident data collected from local police stations. The rudimentary nature of the data required several passes of association rule mining to be performed, each with different sets of parameters, so that semantically interesting rules can be spotted from the pool of results. It was shown that the proposed framework could find candidate rules that offer some insight into the phenomena being studied. © Springer-Verlag Berlin Heidelberg 2007.