Mining And Generating Frequent Patterns For Massive Tourism Data

With the rapid development of tourism industry and mobile Internet,massive tourism data are uploaded to the Internet by tourists.These data contain a lot of interesting and valuable information,which brings new opportunities and challenges to many fields.Pattern mining is an important research topic in the field of big data.Mining meaningful patterns in massive tourism photo data is of great significance to the development and research of tourism information industry.Taking massive tourism data as the research object,this paper explores the problem of finding frequent patterns in tourism data.In the face of large-scale data sets,it is meaningless to directly mine its frequent items.Frequent background information does not have the amount of information.What we need is frequent and discriminative patterns.Firstly,based on the previous research,combined with the clustering algorithm which can effectively deal with large data sets and convolution neural network which has made outstanding achievements in the field of target recognition and image detection,this paper proposes a pattern mining method based on feature density clustering.At the same time,aiming at the problem that the above algorithm can not locate the pattern location,a pattern mining algorithm based on depth dependent feature clustering is proposed.In the experimental stage,this paper sets up several groups of comparative experiments to prove the superiority of the algorithm from two aspects of subjective effect and objective index.The results show that the classification accuracy of the patterns mined by the algorithm is 99.54% on res Net50 network,and the FR(28)index value is 417.17,which has strong discrimination and frequency.On the basis of pattern mining,this paper further explores the generation of frequent patterns,and proposes a pattern generation algorithm based on acgan.The algorithm uses the mining algorithm to obtain the true value data set,and inputs the category label and noise information through acgan,which can reconstruct the frequent and discriminative patterns.The experimental results show that,in terms of relevant indicators,this method has achieved no less than other mining work,and the innovative use of the generated algorithm replaces the traditional mining algorithm,which has a strong academic reference value.The two methods of discovering frequent patterns proposed in this paper provide technical support for the development of smart tourism,and provide more personalized and humanized possibilities for the majority of tourists.At the same time,they contribute to the solution of summarizing the characteristics of large image data sets.