With the rapid development of artificial intelligence, nonlinear time series analysis and mining have become an indispensable part of many disciplines. How to analyze and identify the properties of complex data with higher dimensionality and complexity is still the key problem to be solved in complex system analysis accurately and effectively. For the purpose of helping address this issue, in this paper, we propose a novel nonlinear time series clustering method based on modified stochastic neighbor embedding and improved information dissimilarity measure. This method can effectively reveal the local structure of high dimensional data, so that similar data points can be kept close in low dimensional space. Because it uses probability distribution to calculate the similarity between data points, it can handle nonlinear relations and has strong ability to capture them. Secondly, the method can visualize high-dimensional data effectively, especially when the distribution of data points has a complex structure. It can map high-dimensional data into two-dimensional or three-dimensional space, which is easy to observe and analyze the distribution and clustering of data. In addition, this technique does not need to specify the intrinsic dimensions of the data in advance, and it can automatically learn the intrinsic dimensions of the data, which makes it more flexible in the face of different types of high-dimensional data. In real data applications, similar data of the same class will be clustered together in a low-dimensional space. This makes it excellent in processing high-dimensional data with complex nonlinear structures.
Li et al. (Sun,) studied this question.