Research on Big Data Classification Algorithm Based on Fuzzy Mathematics

Chengyuan Zhao

Zaozhuang Vocational College of Science & Technology Tengzhou, 277599, China

Abstract: In order to improve big data's ability of information fusion and retrieval, it is necessary to optimize the classification design of data. An improved big data classification algorithm is proposed based on fuzzy mathematics. The distributed storage design of database is carried out by using grid topology, and big data's nearest neighbor fuzzy clustering center is calculated by semantic autocorrelation function analysis method, and then the vector quantization feature coding model of big data is constructed. The mass big data characteristic distribution gradient map is extracted, the distributed scheduling input vector value of database is obtained, the self-organization neural network training is used to the cluster coding sample of big data, fuzzy C-means algorithm is used to carry on the big data clustering processing. The method of fuzzy mathematics analysis is used to realize the optimal classification of data. The simulation results show that this method has high accuracy and low error rate in big data classification, and it can improve data fusion and scheduling ability.

Keywords: fuzzy mathematics; big data; classification; database