【正文】 ata structures, evaluate mined patterns, and visualize the patterns in different forms. From a data warehouse perspective, data mining can be viewed as an advanced stage of on1ine analytical processing (OLAP). However, data mining goes far beyond the narrow scope of summarizationstyle analytical processing of data warehouse systems by incorporating more advanced techniques for data understanding. While there may be many “data mining systems” on the market, not all of them can perform true data mining. A data analysis system that does not handle large amounts of data can at most be categorized as a machine learning system, a statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retrieval, including finding aggregate values, or that performs deductive query answering in large databases should be more appropriately categorized as either a database system, an information retrieval system, or a deductive database system. Data mining involves an integration of techniques from mult1ple disciplines such as database technology, statistics, machine learning, high performance puting, pattern recognition, neural works, data visualization, information retrieval, image and signal processing, and spatial data analysis. We adopt a database perspective in our presentation of data mining in this book. That is, emphasis is placed on efficient and scalable data mining techniques for large databases. By performing data mining, interesting knowledge, regularities, or highlevel information can be extracted from databases and viewed or browsed from different angles. The discovered knowledge can be applied to decision making, process control, information management, query processing, and so on. Therefore, data mining is considered as one of the most important frontiers in database systems and one of the most promising, new database applications in the information industry. A classification of data mining systems Data mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science. Moreover, depending on the data mining approach used, techniques from other disciplines may be applied, such as neural works, fuzzy and or rough set theory, knowledge representation, inductive logic programming, or high performance puting. Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques from spatial data analysis, Information retrieval, pattern recognition, image analysis, signal processing, puter graphics, Web technology, economics, or psychology. Because of the diversity of disciplines contributing to data mining, data mining research is exp