Hyperparameter Analysis of the Improved Deep Embedding Clustering Model Clustering has gained large successful in many areas in machine learning especially the computer vision and knowledge discovery. However, the clustering tasks on the highdimension data is still the bottleneck of the clustering. And this problem mainly due to the lack of representative and important features of the data. Recently, the efficient deep clustering models including the deep embedded clustering model (DEC) and the improved deep embedded clustering model (IDEC) are proposed to solve this problem. They validated their models on the realworld datasets and improved the clustering performance compared with the traditional clustering algorithm, which have demonstrated that the superior of DEC and IDEC. Both the clustering method used in DEC and IDEC are studentt distribution, and we choose the IDEC model for further studying the robustness of these two models. Therefore, we discuss how sensitivity of hyperparameter influence in the IDEC model, and comparative experiments are given to show the sensitivities of the hypermeters in IDEC in term of the clustering performances.

Deep Embedded Cluster Tree The idea of combining the high representational power of deep learning techniques with clustering methods has gained much interest in recent years. Optimizing representation and clustering simultaneously has been shown to have an advantage over optimizing them separately. However, so far all proposed methods have been using a flat clustering strategy, with the true number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to know the true number of clusters during optimization. Instead, the level of detail to be analyzed can be chosen afterward and for each subtree separately. An optional dataaugmentationbased extension allows DeepECT to ignore priorknown invariances of the dataset, such as affine transformations in image data. We evaluate and show the advantages of DeepECT in extensive experiments.