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The Application of Binary k-Means Clustering to Identify Groups of Road Traffic Accident?s Factors in United Kingdom

机译:二元k型聚类在英国识别道路交通事故群体中的应用

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Cluster analysis is a formal study of methodsand algorithms for natural grouping or clustering ofobjects according to measured or perceived intrinsiccharacteristics or similarities in each objects. The patternof the each cluster and the relationship for each clusterwere identified and then relate with the frequency ofoccurrence in the data set. This study aims to apply one ofwell-known clustering techniques, k-means clustering intobinary data set in order to cluster the factors of road trafficaccidents as the number of road accidents is increasingfrom day to day. Although there might be a list ofexpected factors that causing the road traffic accidents,none of us known which group of factors that has highestcontribution that lead to road accident. By using k-meansclustering, the patterns of road traffic accidents factorswere identified.
机译:聚类分析是根据测量或感知的每种物体中的特征或相似性的自然分组或群集的方法和算法的正式研究。每个群集的模式和每个群集的关系识别,然后与数据集中的频率相关联。本研究旨在应用一种已知的聚类技术,K-Means聚类流通数据集,以便为道路交通的因素进行聚类,因为道路事故的数量正在增加日期至日。虽然可能有一个突出道路交通事故的遗产的列表,但我们都没有人知道具有能够导致道路事故的最高努力的因素组。通过使用K-meassclustering,鉴定了道路交通事故的因素。

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