Grey incidence clustering method based on dynamic time warping

Grey incidence clustering method is an important research area of grey system analysis. However, current grey incidence clustering methods have some problems when dealing with data sequences with different length. These methods usually choose to pad up the shorter data sequence or delete some redundant data, and that will increase the uncertainty of the system. To solve the problem, this paper proposed a novel grey incidence clustering method by introducing dynamic time warping distance used for unequal-length sequences processing. It can measure the similarity between sequences by computing the shortest path of distance matrix to achieve grey clustering. This method doesn´t need manual intervention. And it possesses stronger robustness. Besides, the experiment shows that the clustering result of this novel method is more correct when handling inconsistent-length data sequences.