Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining and Knowledge Discovery
Next Generation of Data-Mining Applications
Next Generation of Data-Mining Applications
Invited Paper: Intelligent Data Mining Assistance via CBR and Ontologies
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
Mining lung cancer patient data to assess healthcare resource utilization
Expert Systems with Applications: An International Journal
Ontology based data mining: a contribution to business intelligence
MCBE'09 Proceedings of the 10th WSEAS international conference on Mathematics and computers in business and economics
The Knowledge Engineering Review
The Knowledge Engineering Review
Expert Systems with Applications: An International Journal
Evaluation of an integrated Knowledge Discovery and Data Mining process model
Expert Systems with Applications: An International Journal
Domain driven data mining in human resource management: A review of current research
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Various data mining methodologies have been proposed in the literature to provide guidance towards the process of implementing data mining projects. The methodologies describe a data mining project as comprised of a sequence of phases and highlight the particular tasks and their corresponding activities to be performed during each of the phases. It seems that the large number of tasks and activities, often presented in a checklist manner, are cumbersome to implement and may explain why all the recommended tasks are not always formally implemented. Additionally, there is often little guidance provided towards how to implement a particular task. These issues seem to be especially dominant in case of the business understanding phase which is the foundational phase of any data mining project. In this paper, we present an organizationally grounded framework to formally implement the business understanding phase of data mining projects. The framework serves to highlight the dependencies between the various tasks of this phase and proposes how and when each task can be implemented. An illustrative example of a credit scoring application from the financial sector is used to exemplify the tasks discussed in the proposed framework.