Abstract
Whether the characteristics of rural tourism changes or not provides the scale and basis for judging whether the rural tourism landscape has changed, but it cannot provide a judgment on the impact of rural tourism landscape changes. The impact is relative to the rural tourism landscape goal. The determination of rural tourism landscape objectives provides a baseline for judging the direction and impact of rural tourism characteristics and provides a prerequisite for rural tourism landscape actions. The determination of the quality target of the rural tourism landscape is mainly determined by the internal process and external demand of the rural tourism landscape. Through in-depth research on the frequent pattern growth algorithm FP-Growth, the algorithm can find frequent item sets by not generating candidate item sets. The core of the algorithm is the frequent pattern tree FP-tree, which can efficiently compress the transaction database. Based on the advantages of FP-tree, this paper improves a FP_Apriori algorithm based on frequent pattern trees. This algorithm projects the entire transaction database onto the FP-tree, avoiding a lot of I/O overhead. At the same time, I propose a more directional and targeted search strategy for FP-tree, which reduces the running time of the algorithm and uses the principle of the Mapping_Apriori algorithm to prethin the frequent item sets. This article uses the text analysis method of network data to excavate the characteristics and internal structure of rural tourism demand. The rural tourism market has a wide range of needs and multiple levels, and traditional research methods such as questionnaires have limited sample size and sample structure. With the help of network data, text mining, and other statistical analysis methods, in-depth empirical research on the characteristics and spatial structure of rural tourism in a certain region can cover more research groups. The research confirms that the results of using text analysis and questionnaire analysis on the perception of destination image are relatively consistent. Therefore, the network text analysis method is an effective tool to study the demand structure of the rural tourism market.
Highlights
Data mining is to dig out hidden, unknown relationships, patterns, or trends that have potential value to decision makers from large-scale heterogeneous or multisource data and use these knowledge and laws to establish auxiliary decision-making or prediction models [1]
This paper improves an improved Apriori algorithm based on the frequent pattern tree FP-tree
The idea of this improved algorithm is to combine the traditional Apriori algorithm with FP-tree, which can operate on transactions, compress them efficiently, and avoid a large number of repeated transaction database traversals and scans
Summary
Data mining is to dig out hidden, unknown relationships, patterns, or trends that have potential value to decision makers from large-scale heterogeneous or multisource data and use these knowledge and laws to establish auxiliary decision-making or prediction models [1]. It is the process of using various analysis tools to discover models or potential relationships among massive amounts of data. This paper makes a very detailed research and analysis on the improvement ideas of the FP_Apriori algorithm, analyzes the optimization strategy of FP-tree and candidate set frequency calculation, and analyzes the core steps of the algorithm. The overall satisfaction of the market is relatively good, the demand loss trend is obvious
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
