## 4 Experiments and Analysis
### Website Clustering Evaluation  
We trained a deep clustering model based on a multi-layer perceptron using the obtained data representation. The training dataset for the clustering model consisted of 200,000 data points extracted from the total dataset of 3.7 million. To compare with the traditional clustering method, we also conducted a clustering experiment using the k-means algorithm on the same data representation. The experimental results are summarized in Table 1.  
The * in Table 1 indicates the absence of that experimental result, as the K-means algorithm results do not have a concept of confidence. MDC in Table 1 is the abbreviation of the depth clustering model (multimodal deep clustering) used in this paper, The meaning of NMV is \"not modify vision-encoder\", and the representation with NMV uses the representation generated by the model without fine-tuning the parameters of the CLIP image encoder. The meaning of MV is \"modify vision-encoder\", and the representation with MV uses the representation generated by the model with the fine-tuned parameters of the CLIP image encoder. The meaning with img means that only the representation of image data is used in the clustering process, and the meaning with text means that only the representation of text data is used in the clustering process. The meaning of CEloss is to add cross entropy loss to the clustering loss function, and provide some guidance similar to supervised training for the parameters of the clustering model through samples with high confidence. The column Confident-0.999 in the table represents the accuracy of the clustering model when the confidence level for a sample to be divided into a certain category is higher than 0.999. Through the results of this column, it can be seen that, for samples with high confidence, the accuracy of clustering is higher. Figure 7: The lr value of the training representation model. It can be seen from Table 1 that in the group of 'k-means NMV' experiments, the experimental results using only img features are the best, and the effect of combining text features and image features is slightly lower, which also shows the superiority of the original image encoder parameters. In the 'MDC NMV' set of experiments, the experimental results show that the combination of image data representation and text data representation is the best, and the effect is significantly improved compared to only using image data representation and only using text data representation. Compared with the simple use of representation data by the k-means algorithm, the multi-layer perceptron model used by MDC is more capable of processing these complex data. In the group of 'k-means MV' experiments, it can be seen that this is the best combination of image representation and text representation. In the experiment of 'MDC MV', the image encoder parameters are fine-tuned on our data, which makes the performance of the model lower than that of 'MDC NMV'. But, what is interesting here is that the clustering performance using only text representation is the best. This should be because we adjust the parameters of the image encoder, so that the parameters of the text encoder change relatively little, retaining the better text encoder parameters. These sets of clustering experiments actually demonstrate that the model parameters trained on a large dataset do not require fine-tuning for many downstream tasks. Our training representation model uses 3.7 million image-text pair data. However, compared with the training data of the original CLIP image encoder model and the BERT text encoder model, the amount of data is still small. Therefore, we need to add more data to improve the performance of these large pre-training models by fine-tuning. The experimental results unequivocally demonstrate that the utilization of deep clustering methods yields a substantial improvement in accuracy compared to traditional clustering algorithms, such as k-means and DBSCAN.