Research And Application Of AIS Data Mining Based On Improved Spectral Clustering Algorithm

AIS big data contains a lot of important information hidden in the data that cannot be directly obtained by a single device.At present,the mining of AIS data in the fiel d of navigation is still in a shallow mining stage.The mining of AIS data is not suffic ient,and the related factors between ship traffic flows are ignored,and the utilization r ate of multiple data information is low.At present,the focus of the mining of ship AI S data is mainly on the macro performance of ship position information in different po rt spaces.The trajectory time and other factors are still in the stage of assisting decisio n-making information.The ship AIS data has not been fully excavated and properly ap plied.This kind of research still needs to be explored and innovated.This paper comprehensively considers the characteristics of AIS data to represent t he ship's motion state,and adopts a data mining method based on an improved spectral clustering algorithm for ship trajectory clustering and trajectory prediction.The specifi c contents mainly include the following points:(1)Compressing the AIS data during the ship trajectory pre-processing stage,adop t the data compression method of improved Sliding Window algorithm,combine the ch aracteristics of AIS data,adding the ship course as one of the compression variable par ameters,and changing the previous situation that the simple use of latitude and longitu de variables is not in line with the actual nautical conditions and the compression is un reasonable,etc.,which improves the accuracy and speed of data mining and reduces th e memory of AIS trajectory data.(2)Combining the characteristics of spectral clustering algorithm to obtain a more reasonable algorithm complexity and time complexity,improving the traditional clusteri ng method's excessive dependence on parameters,resulting in a situation of local optim ization,and providing support for the effective information mining of ships.(3)Improving the DTW distance so that the comparison results between the traject ories were not affected by the inconsistency of the trajectory length;in the k-means clu stering algorithm,the advantages of density clustering in the DBSCAN clustering algori thm were used,and the idea of point set density was used to select Density peak point,which improves the disadvantage that the simple k-means algorithm is easy to fall into the local optimal.(4)The improved spectral clustering algorithm is applied,and the AIS data of Tia njin Port is used as the sample data for experimental verification.The trajectory cluster ing results are obtained and the main trajectory segments of the water area can be accu rately extracted and divided,providing theoretical support for route identification.(5)Applying the GRU neural network to predict the ship's trajectory.The algorith m has good performance in both time complexity and calculation complexity.Through the auxiliary function of neural network,a scries of data mining results such as ship cl uster trajectory can be verified in reverse.