The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Integrating inductive and deductive reasoning for data mining
Advances in knowledge discovery and data mining
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
An organizational ontology for enterprise modeling
Simulating organizations
Data mining: concepts and techniques
Data mining: concepts and techniques
A user requirements elicitation tool
ACM SIGSOFT Software Engineering Notes
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
Evaluation of decision trees: a multi-criteria approach
Computers and Operations Research
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
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Framework for formal implementation of the business understanding phase of data mining projects
Expert Systems with Applications: An International Journal
Competing on Analytics: The New Science of Winning
Competing on Analytics: The New Science of Winning
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
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The knowledge discovery and data mining (KDDM) process models describe the various phases (e.g. business understanding, data understanding, data preparation, modeling, evaluation and deployment) of the KDDM process. They act as a roadmap for implementation of the KDDM process by presenting a list of tasks for executing the various phases. The checklist approach of describing the tasks is not adequately supported by appropriate tools, which specify ‘how’ the particular task can be implemented. This may result in tasks not being implemented. Another disadvantage is that the long checklist does not capture or leverage the dependencies that exist among the various tasks of the same and different phases. This not only makes the process cumbersome to implement, but also hinders possibilities for semi-automation of certain tasks. Given that each task in the process model serves an important goal and even affects the execution of related tasks due to the dependencies, these limitations are likely to negatively affect the efficiency and effectiveness of KDDM projects. This paper proposes an improved KDDM process model that overcomes these shortcomings by prescribing tools for supporting each task as well as identifying and leveraging dependencies among tasks for semi-automation of tasks, wherever possible.