■ Mining complex data types poses challenging issues, for which there are many dedicated lines of research and development. This chapter presents a high-level overview of mining complex data types, which includes mining sequence data such as time series, symbolic sequences, and biological sequences; mining graphs and networks; and mining other kinds of data, including spatiotemporal and cyber-physical system data, multimedia, text and Web data, and data streams.

■ Several well-established statistical methods have been proposed for data analysis such as regression, generalized linear models, analysis of variance, mixed-effect models, factor analysis, discriminant analysis, survival analysis, and quality control. Full coverage of statistical data analysis methods is beyond the scope of this book. Interested readers are referred to the statistical literature cited in the bibliographic notes (Section 13.8).

■ Researchers have been striving to build theoretical foundations for data mining. Several interesting proposals have appeared, based on data reduction, data compression, probability and statistics theory, microeconomic theory, and pattern discovery–based inductive databases.

■ Visual data mining integrates data mining and data visualization to discover implicit and useful knowledge from large data sets. Visual data mining includes data visualization, data mining result visualization, data mining process visualization, and interactive visual data mining. Audio data mining uses audio signals to indicate data patterns or features of data mining results.

■ Many customized data mining tools have been developed for domain-specific applications, including finance, the retail and telecommunication industries, science and engineering, intrusion detection and prevention, and recommender systems. Such application domain-based studies integrate domain-specific knowledge with data analysis techniques and provide mission-specific data mining solutions.

■ Ubiquitous data mining is the constant presence of data mining in many aspects of our daily lives. It can influence how we shop, work, search for information, and use a computer, as well as our leisure time, health, and well-being. In invisible data mining, “smart” software, such as search engines, customer-adaptive web services (e.g., using recommender algorithms), email managers, and so on, incorporates data mining into its functional components, often unbeknownst to the user.

■ A major social concern of data mining is the issue of privacy and data security. Privacy-preserving data mining deals with obtaining valid data mining results without disclosing underlying sensitive values. Its goal is to ensure privacy protection and security while preserving the overall quality of data mining results.

■ Data mining trends include further efforts toward the exploration of new application areas; improved scalable, interactive, and constraint-based mining methods; the integration of data mining with web service, database, warehousing, and cloud computing systems; and mining social and information networks. Other trends include the mining of spatiotemporal and cyber-physical system data, biological data, software/system engineering data, and multimedia and text data, in addition to web mining, distributed and real-time data stream mining, visual and audio mining, and privacy and security in data mining.