{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:27:26Z","timestamp":1740461246721,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:p>Traffic accident is one of the most important topics in traffic field, and traffic accidents happened in China caused thousands of casualties every year. If we still adopt traditional methods to analyze, we will miss lots of information. As for big data analysis, association rules theory, as a new method of data analysis, has been widely used in various fields. Association rules theory is applied to analyze traffic accidents in this paper. The multi-dimensional data warehouse and star schema are designed in software to store traffic accident data of Guiyang, then the association rules mining model is established and strong association rules in accidents will be obtained. Finally, for these strong association rules in traffic accidents, preventive suggestions will be put forward.<\/jats:p>","DOI":"10.3233\/978-1-61499-939-3-330","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:59:50Z","timestamp":1740398390000},"source":"Crossref","is-referenced-by-count":0,"title":["Traffic Accident Data Mining Based on Association Rules Theory"],"prefix":"10.3233","author":[{"family":"Li Meiying","sequence":"additional","affiliation":[]},{"family":"Li Meiye","sequence":"additional","affiliation":[]},{"family":"Hu Xiaoxia","sequence":"additional","affiliation":[]},{"family":"Li Yu","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Technology and Intelligent Transportation Systems"],"original-title":[],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:17:08Z","timestamp":1740399428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-938-6&spage=330&doi=10.3233\/978-1-61499-939-3-330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-939-3-330","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2019]]}}}