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. Trends and research frontiers in data mining are focused on. An overview of methodologies for mining complex data types is presented. Such mining includes mining time-series, sequential patterns, and biological sequences; graphs and networks; spatiotemporal data, including geospatial data, moving-object data, and cyber-physical system data; multimedia data; text data; web data; and data streams. Other approaches to data mining, including statistical methods, theoretical foundations, and visual and audio data mining are briefly introduced. 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. Data mining applications in business and in science, including the financial retail, and telecommunication industries, science and engineering, and recommender systems are introduced. The social impacts of data mining are discussed, including ubiquitous and invisible data mining, and privacy-preserving data mining. Finally, current and expected data mining trends that arise in response to new challenges in the field e speculated.