本文選題：大數(shù)據(jù) + 客戶(hù)細(xì)分�。� 參考：《北京交通大學(xué)》2015年碩士論文

【摘要】：近年來(lái),我國(guó)鐵路貨運(yùn)信息化建設(shè)取得了很大的突破和成果,但沉淀的大量貨運(yùn)數(shù)據(jù)缺乏有效的管理利用,開(kāi)展大數(shù)據(jù)技術(shù)在鐵路貨運(yùn)業(yè)務(wù)上的數(shù)據(jù)挖掘研究具有重要的應(yīng)用價(jià)值。客戶(hù)細(xì)分是貨運(yùn)營(yíng)銷(xiāo)的基礎(chǔ),能夠更好地識(shí)別客戶(hù)群體,合理地配置企業(yè)資源,為企業(yè)創(chuàng)造更大的利潤(rùn)。但目前鐵路貨運(yùn)的客戶(hù)細(xì)分采用基于經(jīng)驗(yàn)和統(tǒng)計(jì)的簡(jiǎn)單劃分的方法,不能準(zhǔn)確區(qū)分客戶(hù)類(lèi)別,無(wú)法有效地支持營(yíng)銷(xiāo)決策。本文將客戶(hù)細(xì)分的常用方法RFM模型做出改進(jìn),并與聚類(lèi)挖掘算法相結(jié)合,為鐵路貨運(yùn)海量數(shù)據(jù)下復(fù)雜的客戶(hù)細(xì)分問(wèn)題提供了新的解決方法。 本文的主要工作包含以下幾個(gè)方面： (1)針對(duì)鐵路貨運(yùn)的特點(diǎn),對(duì)傳統(tǒng)的客戶(hù)細(xì)分方法RFM模型做了改進(jìn),提出了KFM模型。 (2)由于傳統(tǒng)的K-means聚類(lèi)算法存在對(duì)初始聚類(lèi)中心敏感且容易陷入局部最優(yōu)的缺點(diǎn),本文提出了改進(jìn)的K-means聚類(lèi)算法。實(shí)驗(yàn)表明改進(jìn)后的算法提高了客戶(hù)細(xì)分的準(zhǔn)確率。 (3)將KFM模型與改進(jìn)后的K-means聚類(lèi)算法相結(jié)合,利用鐵路電子商務(wù)系統(tǒng)的貨運(yùn)數(shù)據(jù)進(jìn)行了客戶(hù)細(xì)分。細(xì)分結(jié)果很好地展現(xiàn)了各類(lèi)客戶(hù)的特征,彌補(bǔ)了傳統(tǒng)的基于RFM模型的客戶(hù)細(xì)分對(duì)數(shù)據(jù)挖掘不夠深入的缺陷。 (4)在Hadoop大數(shù)據(jù)平臺(tái)下,實(shí)現(xiàn)了數(shù)據(jù)標(biāo)準(zhǔn)化方法和K-means聚類(lèi)算法基于MapReduce的并行化。實(shí)驗(yàn)表明基于MapReduce的并行化提升了算法的性能,能勝任大量數(shù)據(jù)分析處理任務(wù)。 本文將聚類(lèi)挖掘技術(shù)應(yīng)用于鐵路貨運(yùn)大數(shù)據(jù)平臺(tái)下的客戶(hù)細(xì)分,確定不同價(jià)值和行為傾向的客戶(hù)類(lèi)別,為企業(yè)展現(xiàn)出客戶(hù)所屬類(lèi)別,從而進(jìn)行針對(duì)性管理,有利于貨運(yùn)部門(mén)的精準(zhǔn)化營(yíng)銷(xiāo)決策。
[Abstract]:In recent years, great breakthroughs and achievements have been made in the construction of railway freight information in China, but a large number of freight data precipitated lack of effective management and utilization. It has important application value to develop data mining research of big data technology in railway freight business. Customer segmentation is the basis of freight marketing, which can better identify customer groups, reasonably allocate enterprise resources, and create greater profits for enterprises. However, the current customer segmentation of railway freight is based on the simple division method based on experience and statistics, which can not accurately distinguish customer categories, and can not effectively support marketing decisions. In this paper, the RFM model of customer segmentation is improved and combined with clustering mining algorithm, which provides a new solution to the complex customer segmentation problem under the massive data of railway freight transport. The main work of this paper includes the following aspects: 1) according to the characteristics of railway freight, the traditional customer segmentation method RFM model is improved. Because the traditional K-means clustering algorithm is sensitive to the initial clustering center and easy to fall into local optimum, this paper proposes an improved K-means clustering algorithm. Experiments show that the improved algorithm improves the accuracy of customer segmentation. (3) the KFM model is combined with the improved K-means clustering algorithm, and the freight data of railway e-commerce system is used to segment customers. The segmentation results show the characteristics of all kinds of customers and make up for the defects of traditional RFM-based customer segmentation which is not deep enough for data mining. Data standardization method and K-means clustering algorithm are implemented based on MapReduce parallelization. Experiments show that the parallelization based on MapReduce can improve the performance of the algorithm and be able to deal with a large number of data analysis tasks. In this paper, clustering mining technology is applied to customer segmentation of railway freight big data platform, and customer categories with different values and behavioral tendencies are determined to show customer categories for enterprises, so as to carry out targeted management. It is beneficial to the precision marketing decision of freight department.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP311.13

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本文編號(hào)：2020146