A road accident pattern miner (RAP miner)

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident location...

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Autores principales:	S. M. N. Arosha Senanayake, Sisir Joshi
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	road accident patterns association rules mining frequent pattern case-based reasoning pattern-based classification Telecommunication TK5101-6720 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/39f8fa8d780045c0ad949c67e4a98230
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id	oai:doaj.org-article:39f8fa8d780045c0ad949c67e4a98230
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spelling	oai:doaj.org-article:39f8fa8d780045c0ad949c67e4a982302021-11-17T14:22:00ZA road accident pattern miner (RAP miner)2475-18392475-184710.1080/24751839.2021.1955533https://doaj.org/article/39f8fa8d780045c0ad949c67e4a982302021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/24751839.2021.1955533https://doaj.org/toc/2475-1839https://doaj.org/toc/2475-1847Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.S. M. N. Arosha SenanayakeSisir JoshiTaylor & Francis Grouparticleroad accident patternsassociation rules miningfrequent patterncase-based reasoningpattern-based classificationTelecommunicationTK5101-6720Information technologyT58.5-58.64ENJournal of Information and Telecommunication, Vol 5, Iss 4, Pp 484-498 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	road accident patterns association rules mining frequent pattern case-based reasoning pattern-based classification Telecommunication TK5101-6720 Information technology T58.5-58.64
spellingShingle	road accident patterns association rules mining frequent pattern case-based reasoning pattern-based classification Telecommunication TK5101-6720 Information technology T58.5-58.64 S. M. N. Arosha Senanayake Sisir Joshi A road accident pattern miner (RAP miner)
description	Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.
format	article
author	S. M. N. Arosha Senanayake Sisir Joshi
author_facet	S. M. N. Arosha Senanayake Sisir Joshi
author_sort	S. M. N. Arosha Senanayake
title	A road accident pattern miner (RAP miner)
title_short	A road accident pattern miner (RAP miner)
title_full	A road accident pattern miner (RAP miner)
title_fullStr	A road accident pattern miner (RAP miner)
title_full_unstemmed	A road accident pattern miner (RAP miner)
title_sort	road accident pattern miner (rap miner)
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/39f8fa8d780045c0ad949c67e4a98230
work_keys_str_mv	AT smnaroshasenanayake aroadaccidentpatternminerrapminer AT sisirjoshi aroadaccidentpatternminerrapminer AT smnaroshasenanayake roadaccidentpatternminerrapminer AT sisirjoshi roadaccidentpatternminerrapminer
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A road accident pattern miner (RAP miner)

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