改进FP-Growth算法在旅游线路规划中的应用研究

摘要

针对关联规则挖掘的FP-Growth算法存在对海量数据存储时消耗极大内存开销的弊端,提出一种对FP-Growth加入兴趣度的改进算法,然后与Apriori,FP-Growth算法进行比较,改进后的算法极大减少了内存开销,同时提高了系统执行效率.并且提出改进算法与旅游线路规划挖掘结合的理念,以云南旅游业作为旅游规划对象,充分应用旅游网站的大数据,设计一种旅游线路规划的挖掘系统,为旅游企业找出游客最喜欢的旅游线路以及景区之间的关联规则.%According to the disadvantage of consuming great memory cost in storing massive data existing in the FP-Growth algorithm of mining association rules,the paper presents an improved algorithm which adds interestingness to FP-Growth,and then compares it with Apriori and FP-Growth algorithm,the improved algorithm greatly reduces the memory cost and improves the efficiency of system execution.Based on the idea of combining the improved algorithm with the tourism route planning,taking Yunnan tourism as the tourism planning object and fully applying the large data of the tourism website,the paper designs a mining system for tourism route planning,and finds out the association rules between tourist routes and scenic spots.