Introduction to domain driven data mining
- Publication Type:
- 2009, pp. 3 - 10
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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 (D 3 M for short). This chapter briefs the main reasons, ideas and open issues in D 3 M. © 2009 Springer US.
Please use this identifier to cite or link to this item: