Abstract: The status of marine targets presents a complex and changeable situation. It needs to quickly excavate the group information of marine ships and provide group data support for mastering the situation of marine targets. This paper uses improved FP-growth algorithm to mine marine ships’ data, and uses the method of spatio-temporal segmentation to divide the targets area and mine frequent items. First, the original data is cleaned to get the effective data; secondly, the linear interpolation method is used to process the ship trajectory for subsequent calculation; then, FP-growth algorithm is used to build FP-tree; finally, the frequent term set is obtained to mine the information of marine ship groups. Aiming to the problem of low efficiency of association analysis based on itemset partition, this paper uses Hash table to split database and the method of node exchange to mine frequent itemsets, and compares the efficiency of the algorithm in memory consumption and time consumption. The test is done on AIS data set to verify the efficiency of the improved algorithm, with the given confidence and support of the target group information.

Key words: FP-growth algorithm, marine group targets, spatio-temporal data, Hash table, node exchange