基于改进FP-growth的海上群目标挖掘

Marine Group Target Mining Based on Imporved FP-growth

海上目标状态呈现复杂多变的形势,须快速挖掘海上船舶的群组信息,以掌握海上目标态势。本文使用改进的FP-growth算法对海上船舶进行数据挖掘,使用基于时空分割的方法划分目标区域,挖掘频繁项集。首先清洗原始数据得到有效数据;其次使用线性插值方法处理船舶的轨迹方便后续计算;然后使用FP-growth算法,构建生成FP-tree;最后得到频繁项集,挖掘海上船舶群组信息。针对基于项集划分关联分析查找效率低的问题,本文使用基于Hash表拆分数据库和结点交换的方法挖掘频繁项集,在内存占用和时间消耗两方面比较算法的效率。使用AIS数据集进行验证,在给定的置信度和支持度下挖掘目标群组信息,验证改进算法的高效率。

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.