The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Robust Classification for Imprecise Environments
Machine Learning
Data mining standards initiatives
Communications of the ACM - Evolving data mining into solutions for insights
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data
Data Mining and Knowledge Discovery
SQL multimedia and application packages (SQL/MM)
ACM SIGMOD Record
Post-processing Operators for Browsing Large Sets of Association Rules
DS '02 Proceedings of the 5th International Conference on Discovery Science
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
Knowledge discovery in databases: the purpose, necessity, and challenges
Handbook of data mining and knowledge discovery
Ontological assistance for knowledge discovery in databases process
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Considering application domain ontologies for data mining
WSEAS Transactions on Information Science and Applications
A framework proposal for ontologies usage in marketing databases
MEDI'11 Proceedings of the First international conference on Model and data engineering
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As knowledge discovery (KD) matures and enters the mainstream, there is an onus on the technology developers to provide the technology in a deployable, embeddable form. This transition from a stand-alone technology, in the control of the knowledgeable few, to a widely accessible and usable technology will require the development of standards. These standards need to be designed to address various aspects of KD ranging from the actual process of applying the technology in a business environment, so as to make the process more transparent and repeatable, through to the representation of knowledge generated and the support for application developers. The large variety of data and model formats that researchers and practitioners have to deal with and the lack of procedural support in KD have prompted a number of standardization efforts in recent years, led by industry and supported by the KD community at large. This paper provides an overview of the most prominent of these standards and highlights how they relate to each other using some example applications of these standards.