Research On Tourist Data Mining Method Based On Operator Data

Based on the telecom operators of mobile phone signal data of huge amounts of sex,continuity,authenticity,integrity,timeliness and non-inductive intellectual technology can accurately depict the customer movement characteristics,puts forward the tourists visit scenic spot data mining method based on the carrier data research,establish data mining model of tourists to visit the scenic spot,the tourists visit scenic spot of association rule mining,dug up from mobile phone location signal data to valuable data for tourism industry,for telecom operators to explore new models of data to liquidate help telecom operators to bring considerable economic benefits.Association rules is an important and widely used data mining algorithm.Association rules can mine the potential association relationship from the data.Apriori algorithm is the most representative of the association rule mining algorithm,but the order of the Apriori algorithm does not consider between itemsets,and tourists to visit the scenic spot is orderly,access sequence between the scenic spot is one of the important factors for scenic area recommended,therefore,to introduce the concept of tourism industry focus on his first scenic spot of Apriori algorithm,and analyze the Apriori algorithm itself,found in dealing with a large amount of data when the data set,the results of the support and confidence set out to cause deviation or result set is too large,the practical significance is not big,Therefore,the concept of correlation in probability is combined to transform it.Combined with the above two factors,this paper introduces the concept of relevancy to improve The Apriori algorithm.The Improved algorithm is named I-Apriori(Improved Apriori Algorithm),which is used to dig out strong association rules that are more in line with the actual application scenes.The I-Apriori algorithm also has the disadvantage of low computational efficiency in processing massive data.Therefore,on this basis,the idea of Parallelization computing is introduced to improve the I-Apriori algorithm and improve the computational efficiency of association rule mining.The improved algorithm is finally named PI-Apriori(Parallelization i-Apriori algorithm).After the algorithm is improved,the computational efficiency and accuracy of the PI-Apriori algorithm are verified through experiments.Experiments show that the PI-Apriori algorithm not only ensures the accuracy of the calculation results,but also improves the time efficiency of association rule mining.In order to better verify the practicability of The PI-Apriori algorithm,this paper develops a simple tourist data mining system in scenic spots,named "smart Tourism" system.This system combines the data of real tourists visiting scenic spots,and makes use of PI-Apriori algorithm to predict scenic spots visiting.The system application results show that the smart tourism system can predict scenic spots,and the PI-Apriori algorithm has certain practical significance for tourism big data.