Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems

Application and Research of Rough Sets and Fuzzy Association Rules in Flow Industry

Author LiuJing
Tutor QuShouNing
School Jinan University
Course Applied Computer Technology
Keywords flow industry data mining rough sets theory attribute reduction fuzzy association rules
CLC TP311.13
Type Master's thesis
Year 2010
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In recent years, with constantly evolving of data mining technology, Data Mining becomes increasingly important and has widely applied in many fields. The typical applications of the data mining methods are applied in flow industry. Mainly for two reasons, on one hand, with the development of manufacturing technology, a great amount of historical data of production process is accumulated. A powerful analysis tool is needed to deal with the data. Data mining is the solution for this problem and has found its applications in various areas, such as business, scientific study. On the other hand, the development of information technology and automatic control equipment had created a favorable environment to apply data mining to the flow industry. In view of the features of complexity, binding, non-linear and difficulties of modeling of the actual flow industry, finding suitable methods of data mining has become a major research goal, in order to seek an effective knowledge behind the dry data for decision-making analysis, more convenient and efficient to meet the needs of different objects, such as decision-making or query needs.In this thesis, data mining technology based on rough sets for the process of cement is discussed as follows:Rough sets (RS) theory, which is proposed by Polish mathematician Z. Pawlak in 1982, is a new mathematical tool dealing with the issue of vague and imprecise. Not only can the RS effectively analysis inaccurate, inconsistent and incomplete information, but also can do data analysis and reasoning; it discovers implied knowledge and reveals the potential rules. It has been successfully applied in the machine learning, knowledge discovery, expert system and areas of intelligent control. Its basic thought is to lead out the categorized rule of the concept through knowledge reduction on the premise of keeping categorized ability unchanging. It has important significance for mining relative relationship between parameters and optimizing much parameter in process industry.Association rules reveals the relationship between data items, is an important research area. At the present time, the mining of association rules is mostly applied to the merchandise field. There are also lots of association rules in flow industry. The main topic of this thesis is how to apply the association rules mining to the flow industry and make the rules use of the decision making in the flow industry. This thesis is based on the theory of fuzzy association rules mining proposed by Jian-jiang Lu and found association rules among the attributes, and removed the redundant rules. It can greatly improve the efficiency of data mining and the smooth operation of the device. The results in this paper show that the scheme has merits such as rationality, effectiveness and improving executive efficiency.Association rules mining between control objects can help setting up different control model; it has practical significance and application value for real-time control.

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