Affiliations: [a] Graduate School of Sehan University, Chonnam, Korea | [b] Department of Teaching Profession, Sehan University, Chonnam, Korea
Correspondence:
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Corresponding author: Young Chun Ko, Department of Teaching Profession, Sehan University, Chonnam 58447, Korea. E-mail: ycko@sehan.ac.kr.
Abstract: In order to solve the problems of high computational complexity, poor dimensionality reduction, and reduced clustering effect when the clustering task faces a large amount of big data, the application of a large data adaptive semi-supervised clustering method based on deep learning is proposed. Through the self-encoder of the deep clustering network, the analysis of the confrontation network is generated, and the semi-supervised deep clustering algorithm and algorithm of the adaptive strategy are optimized. Through the encoder layer structure of the deep coding network, different parameters are set for all data sets for algorithm experimental analysis. The results show that the data obtained by this method is faster, more accurate and more optimized than the traditional clustering method, which proves the effectiveness of the method.
Keywords: Big data, deep learning, semi-supervised, learning application
