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铁路货车静载重是衡量货车运用效率的一项重要指标，在线路设计中也是预测静载重系数的重要依据.在对货车静载重指标数据结构进行梳理的基础上，采用K-medorids聚类算法进行数据挖掘，发现铁路局静载重水平具有明显的分级特征，而且各个铁路局在静载重分级中的位置相对稳定.通过因素分析发现静载重不同的货物品类发送比例的差异是引起局间静载重差异的主要影响因素.数据挖掘结果表明，在进行车流折算时区域间的静载重差异不应忽视，宜采用分区域选取静载重系数的方法.该研究成果对于指导铁路货车静载重系数预测具有重要意义.

Data Mining on Index of Static Load of Freight Cars

Static load of freight vehicles is one of the important indices to evaluate the efficiency of railway wagon usage,as well an important basis for predicting coefficient of static load in railway line design.Based on sorting out the data structure of static load,clustering algorithm of K-medorids is employed for data mining to find the significant and relatively stable classification characteristics between static load of railway administrations.It is different delivery proportions of heavy goods that are indicated by index factor analysis to cause the static load classification of railway administrations.The data-mining also indicates the differences of static load between different grading railway administrations which cannot be ignored,therefore determining the coefficient of static load in railway design based on divided region is recommended.