【作者】 刘永；

【导师】 陈治平；

【作者基本信息】 湖南大学 ， 计算机系统结构， 2008， 硕士

【摘要】 随着中国电信市场的逐渐成熟,电信市场竞争日益加剧,电信运营商为了留住老客户、吸引更多的新客户,不断推出新的电信产品。一个新产品从构思、开发、发布和退出市场等整个过程构成了电信产品的生命周期,而在这个周期中的新产品目标客户群的定位、推向市场前的收益预测等环节构成了电信产品生命周期管理中的难题。本文对电信产品生命周期管理模型进行了分析,并且运用数据挖掘的相关知识着重对模型中的客户细分和产品预演进行了研究。根据数据挖掘技术目前的发展现状和本文研究要用到的数据挖掘知识,本文首先分析了数据挖掘过程模型和对聚类算法进行了归纳。在此基础上,本文根据国内电信运营商的相关规范并结合实际项目提出电信产品生命周期管理模型,并对模型中的各个环节进行了具体分析。通过对客户信息进行归类,采用属性约简和数据规范化处理后,本文建立了电信客户细分模型和客户细分评价体系。然而,电信新产品在正式推向市场前没有客户和消费信息,要对其进行市场收益预测等只能借助于现有的电信产品,所以需要进行产品间的相似性计算。本文对电信产品属性进行分析,通过属性特征抽取建立了电信产品相似度计算模型,并利用对象属性相关性的强弱进行属性归并等提出了基于复杂对象分解的相似性度量方法。本文根据某电信运营商的实际数据进行的仿真试验结果表明:基于客户细分模型的K-Means算法在客户细分中有着较好的整体性能,为电信新产品目标客户群的市场定位提供了依据;基于产品相似度计算模型的复杂对象分解的相似性度量方法比传统距离度量方法能更准确的预测新产品的市场情况。更多还原

【Abstract】 The competition of Chinese telecom market becomes intensified more and more with gradully mature of telecom market, and telecom operators provide the new telecom products unceasingly in order to detain the old customers and attract more new customers. A new telecom product process which contains idea, development, deployment, withdraw from the market and so on constitutes the telecom product life cycle (TPLC). But the new product goal customers and the income forecast of new product in the TPLC are a difficult problem. The paper analyzes the model of TPLC management, and using the correlative knowledge of Data Mining researches emphatically the customer segmentation and the product forecast in the TPLC.The paper analyzes first the process model of Data Mining and cluster algorithms according to the current development states of Data Mining technology and the knowledge of Data Mining which will be used in the paper. Based these introduction, the paper proposes the model of TPLC according to the related standard of domestic telecom operators and the actual telecom project , and also analyzes detail each link in the model. Through analysing the customer information, the paper establishes the telecom customer segmentatin model and the appraised system of the model after attribute reduction and data standardizatin processing. But new telecom products have not related object customers and expended information before the new product is deployed normally to market, and the information of the market income forecast of the new product can only obtain by the existing telecom product, so needs to carry on the similarity computing between the products. The paper analyzes the telecom product attributes and establishes the telecom product similarity computing model through the attribute character extraction, and proposes the decomposed similar measure method based on the complex object by combining attributes according to the strong and weak relations between attributes.The result of simulation test which is carried to use actual data of some telecom operators indicates that K-Means algorithm based on the customer segmentation has the good overall performance than other algorithms, and it provides the basis for market localization of new product object customers; The decomposed similar measure method based on the telecom product similarity computing model can forecast market conditions of new product more accurate than the tradition measure methods.更多还原