ABSTRACT
The tremendous volume and diversity of real-world data embedded in huge databases clearly overwhelm traditional manual methods of data analysis, such as spreadsheets and ad-hoc queries. An urgent need exists for a new generation of techniques and tools with the ability to intelligently and automatically assist users in analyzing mountains of stored data for nuggets of useful knowledge. During the last decades, the potential of analytics and data mining methodologies that extract useful and actionable information from large datasets has transformed one field of scientific inquiry after another. Indeed, a data mining method can extract from raw data patterns of interest to the application domain. While these patterns are useful as the starting point of an analytic process, the challenge here is to navigate and explore these patterns in order to come up with a meaningful analysis: an interpretation or a model that can explain the patterns and be used to exploit them as useful insights for decision making. In recent years, the sophistication and ease of use of tools for analyzing data make it possible for an increasing range of researchers to apply data mining methodology.
