Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

Abstract

This paper proposes a multiple collaborative supervision pattern recognition method within social organizations based on data clustering algorithm to realize diversified supervision within social organizations and improve the effect of the said pattern recognition. Firstly, the characteristics and functions of social organizations are analyzed, and the definition of social organizations is given. Further, this paper studies the meaning and characteristics of social organization supervision, analyzes the failure of internal supervision of social organizations, and then determines the internal governance elements of social organizations. In addition, the basic steps of pattern recognition are given. Finally, multiple collaborative supervision patterns recognition within social organizations is realized based on data clustering algorithm. Experiments show that this method can improve the recognition accuracy of multiple collaborative supervision patterns and reduce the recognition time.