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The diagram depicts a data set being broken up into two parts: the characteristics of its ___domain, or ___domain knowledge, and then the actual acquired data. The ___domain characteristics are then processed to become user understood ___domain knowledge that can be applied to the data. Meanwhile, the data set is processed and stored so that the ___domain knowledge can applied to it, so that the process may continue. This application forms the ontology. From there, the ontology can be used to analyze data and process results.
Fuzzy preprocessing is another, more advanced technique for solving complex problems. Fuzzy preprocessing and fuzzy data mining make use of [[fuzzy sets]]. These data sets are composed of two elements: a set and a membership function for the set which comprises 0 and 1. Fuzzy preprocessing uses this fuzzy data set to ground numerical values with linguistic information. Raw data is then transformed into [[natural language]]. Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database. Currently fuzzy preprocessing, as well as other fuzzy based data mining techniques see frequent use with neural networks and artificial intelligence.<ref>{{cite book| chapter=Fuzzy preprocessing rules for the improvement of an artificial neural network well log interpretation model| author=Wong, Kok Wai and Fung, Chun Che and Law, Kok Way| title=2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119)| year=2000| volume=1| pages=400–405| publisher = IEEE | doi=10.1109/TENCON.2000.893697| isbn=0-7803-6355-8| s2cid=10384426
==References==
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