An Improved Density-Based Time Series Clustering Method Based on Image Resampling: A Case Study of Surface Deformation Pattern Analysis

Time series clustering algorithms have been widely used to mine the clustering distribution characteristics of real phenomena. However, these algorithms have several limitations. First, they depend heavily on prior knowledge. Second, the algorithms do not simultaneously consider the similarity of spatial locations, spatial-temporal attribute values, and spatial-temporal attribute trends (trends in terms of the change direction and ranges in addition and deletion over time), which are all important similarity measurements. Finally, the calculation cost based on these methods for clustering analysis is becoming increasingly computationally demanding, because the data volume of the image time series data is increasing. In view of these shortcomings, an improved density-based time series clustering method based on image resampling (DBTSC-IR) has been proposed in this paper. The proposed DBTSC-IR has two major parts. In the first part, an optimal resampling scale of the image time series data is first determined to reduce the data volume by using a new scale optimization function. In the second part, the traditional density-based time series clustering algorithm is improved by introducing a density indicator to control the clustering sequences by considering the spatial locations, spatial-temporal attribute values, and spatial-temporal attribute trends. The final clustering analysis is then performed directly on the resampled image time series data by using the improved algorithm. Finally, the effectiveness of the proposed DBTSC-IR is illustrated by experiments on both the simulated datasets and in real applications. The proposed method can effectively and adaptively recognize the spatial patterns with arbitrary shapes of image time series data with consideration of the effects of noise.