Introduction to Domain Driven Data Mining

The mainstream data mining faces critical challenges and lacks of soft power in solving real-world complex problems when deployed. Following the paradigm shift from ‘data mining’ to ‘knowledge discovery’, we believe much more thorough efforts are essential for promoting the wide acceptance and employment of knowledge discovery in real-world smart decision making. To this end, we expect a new paradigm shift from ‘data-centered knowledge discovery’ to ‘domain-driven actionable knowledge discovery’. In the domain-driven actionable knowledge discovery, ubiquitous intelligence must be involved and meta-synthesized into the mining process, and an actionable knowledge discovery-based problem-solving system is formed as the space for data mining. This is the motivation and aim of developing Domain Driven Data Mining (D3M for short). This chapter briefs the main reasons, ideas and open issues in D3M. 1.1 Why Domain Driven Data Mining Data mining and knowledge discovery (data mining or KDD for short) [9] has emerged to be one of the most vivacious areas in information technology in the last decade. It has boosted a major academic and industrial campaign crossing many traditional areas such as machine learning, database, statistics, as well as emergent disciplines, for example, bioinformatics. As a result, KDD has published thousands of algorithms and methods, as widely seen in regular conferences and workshops crossing international, regional and national levels. Compared with the booming fact in academia, data mining applications in the real world has not been as active, vivacious and charming as that of academic research. This can be easily found from the extremely imbalanced numbers of pubLongbing Cao School of Software, University of Technology Sydney, Australia, e-mail: [email protected].