The Research And Design Of Intelligence Recommendation System Of Traveling Information Service Based On Data Mining

The Data Mining is a fast developing research area in the last decade. It integrates theories and technologies from Database, Artificial Intelligence, Machine Learning, Statistics and so on many domains theory and the technology, has become a bridge between theory study and real world applications.The association rule mining is in a data mining important branch, it contains very many algorithms, the Apriori algorithm is most influential one algorithm, but this algorithm adapts in the single-demension database mining. This article in analyzes the Apriori algorithm in the foundation, the union traveling information service characteristic, has made the improvement to this algorithm, and proposed one kind suits the multi-dimensional database mining the Apriori_MD algorithm, then has carried on the example analysis and the performance analysis to this algorithm.Using the Apriori_MD algorithm, and according to user's in traveling information service concrete demand, this article has constructed the traveling service intelligence recommendation (TSIR) system. This system is an open intelligent recommendation system, can according to the demand which the user proposed, the union user's registration information, browse record to carry on the tour information service the intelligent recommendation.This article first to the TSIR system structure, the main function module has carried on the design. Then, divided six parts multianalyses Apriori_MD algorithm in the TSER system application, founded the corresponding data cube according to the user demand, calculated the frequent predicate collection in the data cube foundation, obtained the frequent predicate collection accumulation the multi-dimensional frequent predicate collection the set, produced the recommendation result collection according to the correlation. Finally through the data analysis, Apriori_MD algorithm in carried out in the efficiency to enhance one time compared to the Apriori algorithm, was one good intelligent recommendation method, might obtain satisfaction according to the user demand the recommendation result.