The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
About The Data Warehousing Institute
Building, using, and managing the data warehouse
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
The unified software development process
The unified software development process
The many dimensions of the software process
Crossroads - Special issue on Windows programming
Networking Explained
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Stages of the discovery process
Handbook of data mining and knowledge discovery
Knowledge discovery in databases: 10 years after
ACM SIGKDD Explorations Newsletter
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Mining and Knowledge Discovery
Toward data mining engineering: A software engineering approach
Information Systems
Framework for formal implementation of the business understanding phase of data mining projects
Expert Systems with Applications: An International Journal
Software Engineering: A Practitioner's Approach
Software Engineering: A Practitioner's Approach
An engineering approach to data mining projects
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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Up to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process named refined data mining process for developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.