Affiliation:
1. Polytechnic Institute of Porto, Portugal
Abstract
The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases. Within the context of inductive databases, a great relevance is given to the so-called DM languages. This chapter explores DM in KDD.
Reference36 articles.
1. Data Mining and Business Intelligence
2. Azevedo, A., & Santos, M. F. (2008). KDD, SEMMA, and CRISP-DM: a Parallel Overview. In H. Weghorn & A.P. Abraham (Eds.), Proceedings of the IADIS European Conference on Data Mining 2008 (pp. 182-185). Amsterdan: IADIS Press.
3. A Perspective on Data Mining Integration with Business Intelligence
4. Closing the Gap between Data Mining and Business Users of Business Intelligence Systems
5. Extending QMBE Language with Clustering
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