Railway Freight Demand Analysis Based on Multidimensional Association Rules Mining

机译：基于多维关联规则挖掘的铁路货运需求分析

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Railway freight demand are predicted and analyzed by using multidimensional association rule based on Apriori algorithm. At the same time, Three correlation models-goods-weight-transport distance, goods-weight-arrival railroad, goods-weight-arrival province are established by using data mining software Clementine to analysis some railway freight invoice of a railway bureau. Finally, some association rules which are helpful for railway freight demand analysis are obtained. It proved that using multidimensional association rules to analyze the railway freight demand is rational and feasible.

机译：利用基于Apriori算法的多维关联规则对铁路货运需求进行预测和分析。同时，利用数据挖掘软件Clementine对某铁路局的部分铁路货运发票进行分析，建立了三种相关模型，即货物重量，运输距离，货物重量到运铁路，货物重量到运省。最后，获得了一些有助于铁路货运需求分析的关联规则。实践证明，利用多维关联规则分析铁路货运需求是合理可行的。

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《International conference on transportation engineering》|2015年|2075-2081|共7页
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Yinying Tang; Yang Qin;
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Railway freight demand; Data mining; Multidimensional association rule;

机译：铁路货运需求;数据挖掘;多维关联规则;

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1. Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules [J] . HONGJUN LU, LING FENG, JIAWEI HAN ACM Transactions on Information Systems . 2000,第4期

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2. Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP [J] . Wang Hairong, Huang Pan, Chen Xu International journal of information technology and web engineering . 2021,第1期

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3. Consumption universes based supermarket layout through association rule mining and multidimensional scaling [J] . Ibrahim Cil Expert Systems with Application . 2012,第10期

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4. Railway Freight Demand Analysis Based on Multidimensional Association Rules Mining [C] . Yinying Tang, Yang Qin International conference on transportation engineering . 2015

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Railway Freight Demand Analysis Based on Multidimensional Association Rules Mining

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