Extending data modeling to cover the whole enterprise
Communications of the ACM - Special issue on analysis and modeling in software development
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
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
Business Modeling and Data Mining
Business Modeling and Data Mining
IEEE Transactions on Knowledge and Data Engineering
Invited Paper: Intelligent Data Mining Assistance via CBR and Ontologies
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Framework for formal implementation of the business understanding phase of data mining projects
Expert Systems with Applications: An International Journal
Design science in information systems research
MIS Quarterly
Evaluating quality of conceptual models based on user perceptions
ER'06 Proceedings of the 25th international conference on Conceptual Modeling
Hi-index | 12.05 |
Data Mining projects are implemented by following the knowledge discovery process. This process is highly complex and iterative in nature and comprises of several phases, starting off with business understanding, and followed by data understanding, data preparation, modeling, evaluation and deployment or implementation. Each phase comprises of several tasks. Knowledge Discovery and Data Mining (KDDM) process models are meant to provide prescriptive guidance towards the execution of the end-to-end knowledge discovery process, i.e. such models prescribe how exactly each one of the tasks in a Data Mining project can be implemented. Given this role, the quality of the process model used, affects the effectiveness and efficiency with which the knowledge discovery process can be implemented and therefore the outcome of the overall Data Mining project. This paper presents the results of the rigorous evaluation of the Integrated Knowledge Discovery and Data Mining (IKDDM) process model and compares it to the CRISP-DM process model. Results of statistical tests confirm that the IKDDM leads to more effective and efficient implementation of the knowledge discovery process.