Stochastic gradient descent based fuzzy clustering for large data

Data is growing at an unprecedented rate in commercial and scientific areas. Clustering algorithms for large data which require small memory consumption and scalability become increasingly important under this circumstance. In this paper, we propose a new clustering approach called stochastic gradient based fuzzy clustering(SGFC) which achieves the optimization based on stochastic approximation to handle such kind of large data. We derive an adaptive learning rate which can be updated incrementally and maintained automatically in gradient descent approach employed in SGFC. Moreover, SGFC is extended to a mini-batch SGFC to reduce the stochastic noise. Additionally, multi-pass SGFC is also proposed to improve the clustering performance. Experiments have been conducted on synthetic data to show the effectiveness of our derived adaptive learning rate. Experimental studies have been also conducted on several large benchmark datasets including real world image and document datasets. Compared with existing fuzzy clustering approaches for large data, the mini-batch SGFC shows comparable or better accuracy with significant less time consumption. These results demonstrate the great potential of SGFC for large data analysis.