Research Of Data Mining In Telecom Product Life Cycle Management

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.