# Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism
## 2 Related Work  
Compared with previous research, this method has made further improvement in clustering, the self-attention mechanism and the deep learning network. Clustering [23] is an important topic in machine learning and data mining. In recent years, deep neural networks (DNNs) [24; 25] have received extensive attention in various clustering tasks. In particular, semi-supervised clustering can significantly improve the clustering performance by introducing only a small amount of prior information into a large number of unsupervised data. However, these clustering methods ignore that the defined clustering loss may destroy the feature space, resulting in non-representative and meaningless features. Aiming at the problem that the existing semi-supervised deep clustering feature learning process has some deficiencies in maintaining the local structure, this paper proposes a deep embedded clustering algorithm, which uses an incomplete automatic encoder to maintain the internal local structure of data during feature expression learning. By combining clustering loss, pair constraint loss and reconstruction loss, the cluster label allocation and feature expression are jointly optimized. The problem of data clustering involves a wide range of scenarios. The purpose of clustering is to divide similar data into a class according to similarity measures (such as Euclidean distance). With the development of science and technology, data collection becomes more convenient, the dimensions of data generated become higher and higher, and the correlation between data becomes more complex. As data sets become larger and more diverse, existing algorithms need to be adjusted to maintain the quality and efficiency of clustering. The traditional clustering algorithm considers all dimensions of the data because the data dimension is not high and in order to obtain all information. However, in high-dimensional data, multiple dimensions of data are usually irrelevant, and these irrelevant dimensions will hide clustering in noisy data, making clustering algorithms confused. In a very high-dimensional dataset, all objects are almost equidistant from each other, thus completely concealing clustering. The feature selection method has been successfully used to improve the clustering quality to some extent. Compared with other complex neural network structures, the structure proposed in this study uses the potential representation form of self-encoder learning data, and then uses this form for clustering. This study made innovations in the potential representation form and fusion mode of extracted data. In cluster analysis, the field of unsupervised machine learning has been studied from different aspects, covering a wide range of scenarios, including deep learning [26; 27], pattern recognition, image processing [28; 29], biological information [30], and so on. How to choose the feature space and how to define the distance measure are the key points of the clustering algorithm. DEC clustering uses an automatic encoder to construct a nonlinear embedding. A clustering layer is stacked on the self-encoder for clustering tasks to achieve parameterized high-dimensional data clustering. Compared with the K-means clustering algorithm, local discrimination and global fusion model, this algorithm greatly improves the effect in clustering tasks, and further enhances the stability of the model. In the self-attention mechanism [31; 32], most of the current research is to design local and global self-attention encoders, and build feature aggregation modules to extract features. The essence of the self-attention mechanism is to highlight or emphasize the important information of the target object through a series of attention weight coefficients, suppress some irrelevant details, and correlate the global information. It is flexible and can directly capture the connection between local and global information.