Tourism Data Clustering Analysis Based on Random Forest Algorithm

Due to the rapid development of the computer and the rapid popularization of the network, people produce a large number of product information on the digital Internet and show an explosive increase, and is moving towards the era of big data analysis. How to use computers to do this, in today’s data age, the effective organization, management and knowledge discovery of seemingly unrelated data is very important, is an important and urgent problem in today’s data age. In order to solve the tourism data clustering of the random forest algorithm, this paper adopts to verify the effect of the improved sample similarity calculation method by using the data set in the UCI database. The results show that, Compared with traditional methods, this method has better fitting and prediction effect.