Data mining method based on rough set and fuzzy neural network

• Published in: Journal of intelligent & fuzzy systems Vol. 38; no. 4; pp. 3717 - 3725
• Main Authors: Zhou, Jingyong, Guo, Yuan, Sun, Yu, Wu, Kai
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 30.04.2020
• Subjects: Artificial neural networks; Data collection; Data mining; Data processing; Decision making; Fault tolerance; Fuzzy logic; Fuzzy sets; Information processing; Learning; Neural networks; Topology; Training
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-179594

With the rapid development of database and Internet technologies, data collection and storage is possible. It is often impossible to correctly analyze the valuable information contained in the data, and it becomes more difficult to obtain valuable information. Therefore, it faces the status of “rich data and scarce knowledge”. Traditional information processing technology can no longer meet the needs of reality. There is an urgent need for more capable and effective information processing skills to help us analyze the information we need from big data and guide us to make the right decisions. Data mining technology is born in the background. Data mining technology is one of the effective methods to solve rich data and improve lack of knowledge. It is also one of the main research topics in the field of information science. Related research and applications have greatly improved people’s decision-making ability. It has been recognized as one of the extremes of data research and has a very broad application prospect. Large databases often contain redundant and unnecessary attributes for many search rules, so the ability to remove duplicate attributes can greatly improve the clarity of potential system knowledge and reduce the time complexity of finding rules. At the same time, it enables efficient operation and improved adaptability. Because the structure of the neural network is variable, it has strong self-organization, self-learning, nonlinearity and high fault tolerance, but the ability to express and interpret knowledge is very poor. The network parameters lack physical meaning and learning. Therefore, it has become an inevitable trend to form a fuzzy neural network combining the characteristics of the two. Therefore, exploring the organic combination between rough sets and fuzzy neural networks is undoubtedly of great significance for data mining technology research. This paper proposes a data mining method based on the combination of rough set and fuzzy neural network technology. Using the approximate set to discover the rules of the database rules, the initial structure of the fuzzy neural network is determined, and the training data is used to train the network. Since the fuzzy neural network thus constructed has a good topology of data distribution features from the beginning, the network scale can be greatly reduced and the network training speed can be improved.