【正文】 ention to the integration of several discovering strategies and techniques and infiltration of multiple subjects. The data mining systems are moving from 1st, 2nd generations into 3rd, 4th generations. Data mining is an application oriented multiplesubject intersectional field and the data mining techniques and theories are motivated by applications. Data mining systems are the bridges between data mining researches and applications and play an important role in popularization of data mining techniques. It is an exigent problem to be solved in the research of data mining systems how to collect existing algorithms under a uniform framework integrating with specific domains and how to construct the data mining systems that can be accepted by different users. In this paper, we have a study on the key techniques in designing 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(cuò)誤 !未找到引用源。而數(shù)據(jù)挖掘系統(tǒng)是數(shù)據(jù)挖掘研究和應(yīng)用的橋梁，對(duì)數(shù)據(jù)挖掘技術(shù)的推廣起到很大的作用。 客戶智能分析系統(tǒng) CIAS ................................. 錯(cuò)誤 !未定義書簽。 實(shí)現(xiàn)的方法 ....................................... 錯(cuò)誤 !未定義書簽。 傳統(tǒng)方法 ......................................... 錯(cuò)誤 !未定義書簽。 問(wèn)題描述 ......................................... 錯(cuò)誤 !未定義書簽。 分類算法研究現(xiàn)狀 ................................. 錯(cuò)誤 !未定義書簽。 本章小結(jié) ............................................. 錯(cuò)誤 !未定義書簽。 擴(kuò)展的 CRISPDM模型 .............................. 錯(cuò)誤 !未定義書簽。 CRISPDM過(guò)程模型及數(shù)據(jù)挖掘商業(yè)定義 ............... 錯(cuò)誤 !未定義書簽。 學(xué)校代碼： 10246 學(xué) 號(hào)： 990307 博 士 學(xué) 位 論 文 數(shù)據(jù)挖掘應(yīng)用平臺(tái)及其關(guān)鍵技術(shù)研究 院 系： 計(jì)算機(jī)與信息技術(shù)系 專 業(yè)： 計(jì)算機(jī)軟件 姓 名： 朱建秋 指 導(dǎo) 教 師： 朱揚(yáng)勇 教授 完 成 日 期 ： 20xx 年 4 月 25 日 數(shù)據(jù)挖掘應(yīng)用平臺(tái)及其關(guān)鍵技術(shù)研究 The Research on A Data Mining Platform and It’s Key Technologies 朱建秋 導(dǎo) 師 朱揚(yáng)勇 教授