A K-means Clustering Optimization Algorithm for Spatiotemporal Trajectory Data

It is a hotspot problem to quickly extract valuable information and knowledge hidden in the complex, different types, fuzzy and huge amount of space-time trajectory data. In the space-time trajectory data clustering method, according to the existing deficiencies of the classical K-means algorithm, the mathematical distance method and effective iteration method are used to select the initial clustering center to optimize the K-means algorithm, which improves the accuracy and efficiency of the algorithm. Based on MATLAB experimental simulation platform, the comparison experiments between the classical algorithm and the optimized algorithm, the applicability test of the performance test, and the comparison test with the classical algorithm were designed. The experimental results show that the optimized K-means randomly selected initial clustering center is more accurate, which can avoid the drawbacks caused by randomly selected initial clustering center to a certain extent and has better clustering effect on sample data, and at the same time avoid the K-means clustering algorithm falling into the dilemma of local optimal solution in the clustering process.