■ Necessity is the mother of invention. With the mounting growth of data in every application, data mining meets the imminent need for effective, scalable, and flexible data analysis in our society. Data mining can be considered as a natural evolution of information technology and a confluence of several related disciplines and application domains.

■ Data mining is the process of discovering interesting patterns from massive amounts of data. As a knowledge discovery process, it typically involves data cleaning, data integration, data selection, data transformation, pattern discovery, pattern evaluation, and knowledge presentation.

■ A pattern is interesting if it is valid on test data with some degree of certainty, novel, potentially useful (e.g., can be acted on or validates a hunch about which the user was curious), and easily understood by humans. Interesting patterns represent knowledge. Measures of pattern interestingness, either objective or subjective, can be used to guide the discovery process.

■ We present a multidimensional view of data mining. The major dimensions are data, knowledge, technologies, and applications.

■ Data mining can be conducted on any kind of data as long as the data are meaningful for a target application, such as database data, data warehouse data, transactional data, and advanced data types. Advanced data types include time-related or sequence data, data streams, spatial and spatiotemporal data, text and multimedia data, graph and networked data, and Web data.

■ A data warehouse is a repository for long-term storage of data from multiple sources, organized so as to facilitate management decision making. The data are stored under a unified schema and are typically summarized. Data warehouse systems provide multidimensional data analysis capabilities, collectively referred to as online analytical processing.

■ Multidimensional data mining (also called exploratory multidimensional data mining) integrates core data mining techniques with OLAP-based multidimensional analysis. It searches for interesting patterns among multiple combinations of dimensions (attributes) at varying levels of abstraction, thereby exploring multidimensional data space.

■ Data mining functionalities are used to specify the kinds of patterns or knowledge to be found in data mining tasks. The functionalities include characterization and discrimination; the mining of frequent patterns, associations, and correlations; classification and regression; cluster analysis; and outlier detection. As new types of data, new applications, and new analysis demands continue to emerge, there is no doubt we will see more and more novel data mining tasks in the future.

■ Data mining, as a highly application-driven domain, has incorporated technologies from many other domains. These include statistics, machine learning, database and data warehouse systems, and information retrieval. The interdisciplinary nature of data mining research and development contributes significantly to the success of data mining and its extensive applications.

■ Data mining has many successful applications, such as business intelligence, Web search, bioinformatics, health informatics, finance, digital libraries, and digital governments.

■ There are many challenging issues in data mining research. Areas include mining methodology, user interaction, efficiency and scalability, and dealing with diverse data types. Data mining research has strongly impacted society and will continue to do so in the future.