Data mining is an (66) 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 (67) , artificial (68) 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 (69) , the evaluation methods of classification rules are presented. Existing outlier mining approaches are introduced which include outlier mining approach based on statistics, distance-based outlier 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 (70) of data mining are described.
（69）处填（）。
A．shortcoming
B．disadvantage
C．localization
D．improvement
