Clustering Model Based on RBM Encoding in Big Data

Abstract

In this paper, a clustering model based on deep learning RBM encoding is proposed for the further data mining of the massive, complex and high-dimensional data. This model includes two major parts: pre-training and fine-tuning & optimization. In the pre-training part, proper parameters are adopted for RBM encoding to reduce the high-dimensional and large-scaled data, and then pre-clustering is done with k-means and other algorithms. The fine-tuning & optimization part is developed from the deep structure of pre-training to form a deep fine-tuning, and network is initialized with the parameters generated from the pre-training, and then the initial clustering center generated from pre-training process is further clustered and optimized. At the same time, encoding features are optimized and the final clustering center and membership matrix are obtained. In order to validate this model, some data are selected from the UCI dataset for clustering comparison. It is indicated in the data analysis that this clustering model based on RBM encoding has little impact on the clustering effect, but the execution is more efficient.