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

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 associati...

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Bibliographic Details
Main Author:	Rangsipan Marukatat
Other Authors:	Mahidol University
Format:	Conference or Workshop Item
Published:	2018
Subjects:	Computer Science Engineering
Online Access:	https://repository.li.mahidol.ac.th/handle/123456789/24379
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Institution:	Mahidol University

id	th-mahidol.24379
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spelling	th-mahidol.243792018-08-24T08:49:09Z Structure-based rule selection framework for association rule mining of traffic accident data Rangsipan Marukatat Mahidol University Computer Science Engineering 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. © 2006 IEEE. 2018-08-24T01:48:01Z 2018-08-24T01:48:01Z 2007-12-01 Conference Paper 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006. Vol.1, (2007), 781-784 10.1109/ICCIAS.2006.294241 2-s2.0-38549181507 https://repository.li.mahidol.ac.th/handle/123456789/24379 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38549181507&origin=inward
institution	Mahidol University
building	Mahidol University Library
continent	Asia
country	Thailand Thailand
content_provider	Mahidol University Library
collection	Mahidol University Institutional Repository
topic	Computer Science Engineering
spellingShingle	Computer Science Engineering Rangsipan Marukatat Structure-based rule selection framework for association rule mining of traffic accident data
description	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. © 2006 IEEE.
author2	Mahidol University
author_facet	Mahidol University Rangsipan Marukatat
format	Conference or Workshop Item
author	Rangsipan Marukatat
author_sort	Rangsipan Marukatat
title	Structure-based rule selection framework for association rule mining of traffic accident data
title_short	Structure-based rule selection framework for association rule mining of traffic accident data
title_full	Structure-based rule selection framework for association rule mining of traffic accident data
title_fullStr	Structure-based rule selection framework for association rule mining of traffic accident data
title_full_unstemmed	Structure-based rule selection framework for association rule mining of traffic accident data
title_sort	structure-based rule selection framework for association rule mining of traffic accident data
publishDate	2018
url	https://repository.li.mahidol.ac.th/handle/123456789/24379
_version_	1763497716923498496

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

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