本文针对经典粗糙集模型存在的对噪音数据敏感等缺陷，提出了基于隶属度和特征距离的β-δ0拓展模型，该模型不仅能够处理含有噪音的不完全、不精确信息系统，其结果也能反映大量数据所满足的统计规律；针对β-δ0拓展模型，提出了一种基于属性上下文敏感度的启发式属性约简算法。

介绍了决策树的概念和生成过程，仔细研究了决策树的几种技术，并将它们集成到作者的决策树分析系统中。在分析了医疗数据之后，提出了今后决策树研究的一个重要方向。

Web usage mining is the process of applying data mining technique to the discovery of user behavior patterns based on Web log data, for various applications. In the advance of e-commerce, the importance of Web usage mining grows larger than before. As an important research field of Web usage mining, mining users navigation patterns is the fundamental approach for optimizing the frame design of Web site. In this paper, we propose an ant colony approach for users navigation patterns. Firstly, a Web site model is built. Secondly, a navigation model is established, based on ant colony approach and Web logs. Finally, we design an algorithm regarding all users as entity for mining their preferred navigation paths.

In the advance of e-commerce, the importance of predicting the next request of a user as he or she visits Web pages grow1s larger than before. Web usage mining is the process of applying data mining technique to the discovery of user behavior patterns based on Web log data, well suited to this problem. As an important research field of Web usage mining, mining user navigation patterns is the fundamental approach for generating recommendations. In this paper, we propose an ant colony approach for user navigation patterns. In our approach, we consider the web users as artificial ants, and use the ant theory as a metaphor to guide user’s choice in the Web site. Firstly, a Web site model is built. Secondly, a navigation model is established, based on ant colony approach and Web logs. Finally, we design an algorithm regarding all users as entity for mining their preferred navigation patterns.

在以信息为中心，竞争日益激烈的今天，人们在决策时对即时准确的知识的需求日趋明显。然而，处理这些海量数据、以简洁的形式将有价值的信息提供给决策者的技术尚不成熟，因此需要有新技术来支持管理者的战略决策。数据挖掘正是为解决上述问题而出现的一项新技术。规范数据挖掘目标、选择适当的挖掘方法是数据挖掘过程中最重要的步骤。本文系统地总结了数据挖掘所能实现的目标及其相关的技术和方法，并提出了未来数据挖掘领域进一步研究和发展的方向。