数据挖掘技术是当前数据库和人工智能领域研究的热点课题，为了使人们对该领域现状有个概略了解，在消化大量文献资料的基础上，首先对数据挖掘技术的国内外总体研究情况进行了概略介绍，包括数据挖掘技术的产生背景、应用领域、分类及主要挖掘技术；结合作者的研究工作，对关联规则的挖掘、分类规则的挖掘、离群数据的挖掘及聚类分析作了 较详细的论述；介绍了关联规则挖掘的主要研究成果，同时指出了关联规则衡量标准的不足及其改进方法，提出了分类模式的准确度评估方法；最后，描述了数据挖掘技术在科学研究、金属投资、市场营销、保险业、制造业及通信网络管理等行业的应用情况，并对数据挖掘技术的应用前景作了展望。

Data mining is an emerging research field in database and artificial intelligence. In this paper, the data mining techniques are introduced broadly including its producing background, its application and its classification. The principal techniques used in the data mining are surveyed also, which include rule induction, decision tree, artificial neural network, genetic algorithm, fuzzy technique, rough set and visualization technique. Association rule mining, classification rule mining, outlier mining and clustering method are discussed in detail. The research achievements in association rule, the shortcomings of association rule measure standards and its improvement, the evaluation methods of classification rules are presented. Existing outlier mining approaches are introduced which include outlier mining approach based on statistics, distance|based outler mining approach, data detection method for deviation, rule|based outlier mining approach and multi|strategy method. Finally, the applications of data mining to science research, financial investment, market, insurance, manufacturing industry and communication network management are introduced. The application prospects of data mining are described.