基于数据挖掘的铁路车站信号平面布置图信息提取

针对铁路车站信号平面布置图因数据格式不同而导致其数据无法二次利用的问题，提出基于数据挖掘的车站信号平面布置图信息提取方法。文章构建铁路信号工程图例模型与编码，基于RVDBSCAN算法，将图形数据聚类为图形组；通过C4.5决策树构建模型，以识别铁路信号图例。试验结果表明，聚类方法 FMI评分0.986 0，分类算法准确率95.64%，能够准确识别布置图中的图例符号数据，为布置图信息的二次利用提供了数据通用接口。

Information extraction of railway station signal layout plan based on data mining

AAiming at the problem that the data of railway station signal layout plan cannot be reused due to different data formats, this paper presented a data mining based method for extracting information from railway station signal layout plan. The paper constructs the model and code of railway signal engineering legend, and clusters the graphic data into graphic groups based on RV-DBSCAN algorithm, constructed a model through C4.5 decision tree to identify railway signal legend. The test results shows that the FMI score of the clustering method is 0.9860, and the accuracy rate of the classification algorithm is 95.64%. It can accurately identify the legend symbol data in the layout plan, and provides a general data interface for the secondary use of the layout plan information.