Traditional data mining research mainly focuses on developing, demonstrating, and pushing the use of specific algorithms and models.Patterns that are actionable to business often are hidden in large quantities of data with complex data structures, dynamics, and source distribution. A sleek data mining methodology termed as domain driven data mining has been introduced to develop a practical data mining methodology which can advise the process of real-world data analysis and preparation, the selection of features, the design and fine-tuning of algorithms, and the evaluation and refinement of mined results.
The process of data mining indicates at pattern identification. A widely seen fact is that
- Many algorithms have been designed of which very few are repeatable and executable in the real world.
- Most often many patterns are mined but a major proportion of them are either common sense or of no particular interest to business.
- End users generally cannot easily understand and take them over for business use.
Key Components that support Domain-Driven Data Mining
- Constraint based context: Actionable knowledge can be discovered in a constraint-based context such as environmental reality, expectations, and constraints in the mining process.
- Integrating Domain Knowledge: The integration of domain knowledge is subject to how it can be represented and filled in to the knowledge discovery process. Domain knowledge in the business field often takes forms of precise knowledge, concepts, beliefs, relations, or vague preference and bias.
- Enhancing Knowledge Action ability: The measurement of actionable patterns is to follow the action ability of a pattern. Both technical and business interestingness measures must be satisfied from both objective and subjective perspectives.
- Reference Model and Questionnaire: Reference models are very helpful for guiding and managing the knowledge discovery process. It is recommended that those reference models must be respected in domain-oriented, real-world data mining. However, actions and entities for domain-driven data mining, such as considering constraints and integrating domain knowledge, should be paid special attention in the corresponding models and procedures.
Most of the organizations focus on adopting the domain driven data mining technology to their implementation. The online trading business is also doing great because of these latest technologies only. Real-world data mining applications have proposed urgent requests for discovering actionable knowledge of main interest to real-user and business needs. Websites like Urban Ladder are also making use if the domain driven technology to maximize their outputs. Knowledge action ability is highlighted in the discovery process. Both technical and business interestingness must be concerned in order to satisfy needs and especially business requests.
In summary, we see that the findings are not actionable and lack soft power in solving real-world complex problems. Thorough efforts are essential for promoting the action ability of knowledge discovery in real-world smart decision making. To this end, domain-driven data mining has been proposed to tackle the above issues, and promote the paradigm shift from data-centred knowledge discovery to domain-driven, actionable knowledge delivery. In Domain driven data mining, ubiquitous intelligence is incorporated into the mining process and models, and a corresponding problem-solving system is formed as the space for knowledge discovery and delivery.