A temporal clustering method fusing deep convolutional autoencoders and dimensionality reduction methods and its application in air quality visualization

Abstract

With the development of technologies, big data has become a common feature in all fields, and many of them are time-series data. To find out the data regular over time, effective feature extraction methods and visualization methods are required. In this paper, we firstly use the deep convolutional auto-encoder method to extract the features in the specified time range. Then, the dimension of the extracted features is reduced using different dimension reduction methods, and the results are presented by 2D projection. The results show the distributions of different time-series patterns. Air quality data of Hong Kong are used to verify the effectiveness of the method, and the method is also compared with different dimensionality reduction methods directly, and the proposed method in our paper shows great advantages in time clustering.