The purpose of unsupervised clustering is to divide the data into meaningful or useful clusters according to the distance in the representation space,The different categories are overlap-ping each other in the representation space,In order to achieve a good separation of different catego-ries,it can use an example contrast learning model(SCCL),on the basis of the SCCL model,the activation function of the model is modified to Tanh,The Single-Layer Perceptron(SLP)was modi-fied to a multilayer perceptron,and a Clustering with Deep Contrastive Learning Model(CDCL)was proposed.The model first inputs the original Chinese long text dataset into the neural network fea-ture extraction layer Bert,and then inputs all the extracted features into the Instance-wise Contras-tive Learning(Instance-CL)layer to optimize the features,and finally use K-means for clustering.The performance of the deep contrast learning clustering model CDCL in Chinese long text clustering is evaluated,and it is shown that the deep contrast learning clustering model CDCL improves the ac-curacy of unsupervised clustering by 10％-25％compared with unsupervised clustering on the THUCNews dataset.The results show that the model can better promote the effective separation of different categories of overlapping data,and the experimental effect is significantly better than other existing related models.
万方数据
维普
