Publication:
Structure-based rule selection framework for association rule mining of traffic accident data

Issued Date

2007-12-01

Resource Type

Conference Paper

ISSN

16113349
03029743

Other identifier(s)

2-s2.0-38349033662

Rights

Mahidol University

Rights Holder(s)

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/20.500.14594/24382

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Title

Structure-based rule selection framework for association rule mining of traffic accident data

Author(s)

Rangsipan Marukatat

Other Contributor(s)

Mahidol University

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.

Keyword(s)

Computer Science
 Mathematics

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/24382

Collections

Scopus 2006-2010