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http://dx.doi.org/10.5391/IJFIS.2003.3.2.194

Intelligent Methods to Extract Knowledge from Process Data in the Industrial Applications  

Woo, Young-Kwang (School of Electrical and Computer Engineering, Pusan National University)
Bae, Hyeon (School of Electrical and Computer Engineering, Pusan National University)
Kim, Sung-Shin (School of Electrical and Computer Engineering, Pusan National University)
Woo, Kwang-Bang (Automation Technology Research Institute, Yonsei University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.3, no.2, 2003 , pp. 194-199 More about this Journal
Abstract
Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.
Keywords
Knowledge ＆ Rule Extraction; Clustering; Neuro-Fuzzy; Extension Matrix;
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