铁路货运信息数据挖掘研究

发布时间：2018-03-28 19:16

本文选题：货运数据　切入点：数据挖掘　出处：《中南大学》2012年硕士论文

【摘要】：随着货运市场竞争的不断加剧、铁路信息化进程的逐步推进,铁路信息系统积累了大量的货运数据信息。货运数据具有信息量大、结构复杂、多层次等特征。应用数据挖掘相关技术,研究铁路货运市场目标客户、生命周期、客户价值及发展等相关问题,是当前铁路货运管理研究的热点话题。论文研究了我国铁路货运数据挖掘问题,所做主要工作如下： (1)分析了铁路货运数据的组成、特点和层次结构,并对铁路货运数据进行了整理和分类。 (2)深度挖掘铁路货运数据关联规则,进行知识发现；对铁路货运数据进行聚类分析,探寻铁路货运目标客户；利用ARIMA模型,提出基于时间序列的铁路货运量预测方法。 (3)系统分析铁路货运客户关系生灭过程,合理划分铁路货运客户生命周期的典型阶段,揭示不同阶段特征变化和客户忠诚发展演变规律,构建基于数据挖掘的铁路货运客户生命周期阶段判定模型,提出铁路货运客户生命周期阶段判定过程与方法。 (4)深入分析不同生命周期阶段铁路货运客户利润曲线特征,构建不同阶段的铁路货运客户利润拟合函数,并提出基于数据挖掘的铁路货运客户价值细分算法,对铁路货运客户进行细分。 (5)全面分析营销成本、客户类型、期望收益等铁路货运客户发展影响因素,构建潜在型客户发展模型、竞争型客户发展模型以及保持型客户发展模型等一系列客户发展模型,在此基础上,提出不同类型不同阶段的铁路货运客户关系管理策略,并给出实例分析。
[Abstract]:With the increasing competition of freight transportation market and the gradual advancement of railway informatization process, railway information system has accumulated a large amount of freight data information, which has a large amount of information and complex structure. Using data mining technology to study the target customer, life cycle, customer value and development of railway freight market. It is a hot topic in the research of railway freight management. This paper studies the data mining problem of railway freight transport in China. The main work is as follows:. 1) the composition, characteristics and hierarchical structure of railway freight data are analyzed, and the railway freight data are arranged and classified. (2) deeply mining the association rules of railway freight data, making knowledge discovery; clustering analysis of railway freight data, searching for target customers of railway freight; using ARIMA model, a method of railway freight volume prediction based on time series is proposed. 3) systematically analyzing the birth and death process of railway freight transport customer relationship, reasonably dividing the typical stages of railway freight customer life cycle, revealing the characteristics of different stages and the evolution law of customer loyalty development. Based on data mining, a decision model of railway freight customer life cycle stage is built, and the process and method of railway freight customer life cycle phase determination are proposed. 4) deeply analyzing the characteristics of railway freight customer profit curve in different life cycle stages, constructing the railway freight customer profit fitting function in different stages, and putting forward the railway freight customer value subdivision algorithm based on data mining. Subdivide railway freight customers. 5) analyzing the influence factors of railway freight customer development, such as marketing cost, customer type and expected income, and constructing a series of customer development models, such as potential customer development model, competitive customer development model and maintenance customer development model, etc. On this basis, the paper puts forward different types and different stages of railway freight customer relationship management strategy, and gives an example analysis.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F252;F532;F224

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