博士論文數(shù)據(jù)挖掘應用平臺及其關鍵技術研究-資料下載頁
2025-06-13 17:03本頁面
【導讀】CRISP-DM過程模型及數(shù)據(jù)挖掘商業(yè)定義...............錯誤!
【正文】 and implementing 3rd generation data mining systems and propose a uniform framework, design and implement a Data Mining Application Platform based on 3rd generation techniques. It may be a theoretical and practical guidance for the construction and development of data mining systems. The majority of our work is summarized here: 1) Propose the conclusion to break the development of data mi ning systems into four generations from technique aspect and three phases from evolution aspect, then induce the trend that data mining systems should be integrated with applications, and bring forward the concept of Data Mining Application Platform. 2) Design a novel data mining system architecture that divides data mining into five layers: data layer: algorithm layer, business rule layer, business presentation layer. In this article, we extend the CRISP_DM data mining process model by adding process model’s support to user role and closed loop, then design the framework and architecture of Data Mining Application Platform. We conclude that the universal platform cannot solve the problem in specific domain and we should construct the application platform through 錯誤 !未找到引用源。 數(shù)據(jù)挖掘應用平臺及其關鍵技術研究 復旦大學博士學位論文 4 integrating with business rules, then implement in specific applications. 3) Improve and optimize some data mining algorithms, improve the performance and applicable range of the algorithms. We bring forward the association rule algorithm with negative attributes and sequential pattern algorithm with time characteristic— TESP. The association rule algorithm with negative attributes introduces interesting as the criterion of evaluation and makes some improvements to be able to mine association rules with nega tive attributes. TESP introduces the concept of sequential pattern’s time characteristic, it gives the time characteristic of sequential patterns when finding the patterns and it also allows user to put some restricts on the time characteristic of sequential patterns in order to improve the usefulness and flexibility of sequential pattern mining. We integrate the geic algorithms with BP neural work and design a geic based backpropagation neural work classifier. We make some optimization on the design and implementation of decision tree algorithm— SLIQ and automatic outlier detection algorithm— LOF. 4) Propose the architecture of designing the business rule layer in customer relationship management (CRM), make use of data mining techniques to build customer behavior models, design and implement five operation model: product remendation, customer acquisition, customer attrition, customer value, customer response. 5) Design and implement a data ETL tool— DMETL, an association rule tool— ARMiner, a data mining tool set— DMiner and a customer intelligent analysis system— CIAS. Key Words: Data Mining Application Platform, business rule, business model, customer behavior modeling, ponen
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