本文關鍵詞：聚類分析在港口客戶細分中的應用　出處：《北京交通大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： K-means算法 AP算法 PSO算法 港口客戶細分

【摘要】：隨著國內外港口競爭不斷加劇和港口自身業(yè)務的發(fā)展,要求國內港口企業(yè)的運營模式,必須逐步向以信息為基礎、以數據為中心、以客戶為中心的國際先進模式進行轉變,而實現這種科學經營模式的前提需要進行客戶細分工作的研究。目前中國港口企業(yè)進行客戶細分的方法還是基于統(tǒng)計或者基于經驗的簡單分類方法,并沒有實現企業(yè)與客戶之間真正的信息交互,無法滿足針對不同客戶需求而提供不同的服務策略。 聚類分析作為數據挖掘技術中的一種重要方法,已經成為該領域中一個非常重要的研究內容。聚類分析是在沒有任何先驗知識的情況下將一批樣本數據(或變量)按照它們在性質上的親疏程度自動進行分類,最終能夠實現樣本空間的盲分類。其次使用數據挖掘聚類分析方法進行客戶細分,不但可以處理幾十、甚至上百個變量,從而對客戶進行更精準的描述,客觀地反映客戶分組內的特性并綜合反映客戶多方面的特征；而且還有利于營銷人員更加深入細致地了解客戶特征,便于實現對客戶行為變化的動態(tài)跟蹤；進而實現對客戶提供差異化服務,提高客戶的滿意度和忠誠度,使企業(yè)創(chuàng)造更多價值。 本文在現有的港口信息化背景下,首先闡述了在信息化推進到現今的階段港口生產數據對于分析與挖掘功能的迫切需求和使用數據挖掘技術的必要性。然后對客戶細分基本理論、聚類分析方法應用于客戶細分的基本理論以及相關的聚類分析算法做了詳細的概述,為后文在進行客戶細分中應用聚類分析方法奠定了理論基礎。分析港口客戶數據庫的情況,選擇和構造了港口客戶細分所需要的屬性,并對其進行預處理,為客戶細分研究的展開做好數據準備。其次著重分析了傳統(tǒng)的經典聚類算法K-means、AP算法和粒子群3種算法在港口客戶細分中的不足,提出了融合3種算法優(yōu)點的混合型聚類算法,該算法利用AP算法進行K值的選取,并充分利用PSO算法的全局搜索能力強與K-means算法局部搜索能力強等特性,通過實驗驗證了本文的算法能夠提高聚類的效果和準確率,加快算法的收斂速度。最后將改進的K-means聚類算法應用到港口生產業(yè)務的管理實踐之中,對客戶細分結果進行解釋,分析每類細分市場的特征,結合港口的實際情況,針對現有的客戶,給出相應的客戶營銷目標與策略,并提出了開發(fā)新客戶市場的建議。
[Abstract]:With the development of domestic and international competition intensifies and their business port port, port requirements of domestic enterprises operating mode, turn to the information based, data centric, customer centered international advanced mode transformation, and realize the premise of this scientific management mode of the research needs of customer segmentation work. At present China port customer segmentation is based on the statistical classification method based on experience or simple, and no real information interaction between enterprises and customers, to meet different customer needs and provide different service strategies.
Clustering analysis is an important method of data mining technology, has become a very important research content in the field. Cluster analysis is without any prior knowledge of the case will be a number of sample data (or variables) according to their degree of affinity in the nature of automatic classification, finally can realize blind classification sample space. Secondly using data mining clustering analysis method for customer segmentation, not only can handle dozens, or even hundreds of variables, and thus a more accurate description of the customer, objectively reflect the characteristics of the customer group and reflect various customer characteristics; but also conducive to the marketing personnel more deeply understand customers features, easy to realize dynamic tracking changes in customer behavior; and can provide customers with differentiated services, improve customer satisfaction and loyalty, the creation of enterprises Make more value.
In this paper, the background of existing port information, firstly expounds the necessity of information in advance to the demand and use of data for the current stage of the port production data mining function analysis and mining technology. Then the basic theory of customer segmentation, clustering analysis method was applied to the basic theory of customer segmentation and clustering analysis algorithm is made. Detailed summary, analysis method lays a theoretical foundation of the application of clustering in customer segmentation. In the analysis of port customer database, select and construct attribute port customer segmentation is needed, and carries on the pretreatment for customer segmentation research on data preparation. Then focuses on the analysis of the classical K-means clustering the traditional algorithm, AP algorithm and particle swarm algorithm in 3 port customer segmentation, we propose a hybrid algorithm has the advantages of integration of 3 kinds of Poly Class of algorithms, the algorithm selects the K value by using AP algorithm, and make full use of the global search ability of PSO algorithm and K-means algorithm local search ability and other characteristics, the experimental results indicate that this algorithm can improve the clustering performance and accuracy, accelerate the convergence of the algorithm. Finally, the improved K-means clustering algorithm to the management practice of port production business, for the interpretation of the results of customer segmentation, analysis of each type of market characteristics, combined with the actual situation of the port, for existing customers, the corresponding customer marketing objectives and strategies, and put forward to develop the new customer market.

【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F552.6;F274;F224

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本文編號：1380547