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    Design of Smart Tourism Data Mining Technology Based on Optimized Apriori Association Rule

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    https://www.riss.kr/link?id=A110295518

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    With the widespread application of information technology, smart tourism has emerged as an important direction for the development of the tourism industry. To improve the technological level of the smart tourism industry, a technology based on optimized Apriori association rule mining algorithm is designed. The Spark in-memory computing framework is chosen as the distributed programming framework to store elastic distributed datasets in distributed memory. The support is used to measure the probability of two scenic spots appearing simultaneously, and the order of tourists visiting the scenic spots is considered. A complete data mining process is designed. In the test of the association rules, the research method generated 4,271 association rules at a confidence level of 0.056. When the minimum support threshold of the research method was 30%, the number of invalid frequent itemsets generated was only 831. In the analysis of operational accuracy, the research method maintained an accuracy of over 98.8% when analyzing a data volume of 10,000. This indicates that the research method has good data mining performance and faster computation speed during runtime. The research method can provide good technical support for smart tourism.
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    With the widespread application of information technology, smart tourism has emerged as an important direction for the development of the tourism industry. To improve the technological level of the smart tourism industry, a technology based on optimiz...

    With the widespread application of information technology, smart tourism has emerged as an important direction for the development of the tourism industry. To improve the technological level of the smart tourism industry, a technology based on optimized Apriori association rule mining algorithm is designed. The Spark in-memory computing framework is chosen as the distributed programming framework to store elastic distributed datasets in distributed memory. The support is used to measure the probability of two scenic spots appearing simultaneously, and the order of tourists visiting the scenic spots is considered. A complete data mining process is designed. In the test of the association rules, the research method generated 4,271 association rules at a confidence level of 0.056. When the minimum support threshold of the research method was 30%, the number of invalid frequent itemsets generated was only 831. In the analysis of operational accuracy, the research method maintained an accuracy of over 98.8% when analyzing a data volume of 10,000. This indicates that the research method has good data mining performance and faster computation speed during runtime. The research method can provide good technical support for smart tourism.

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    목차 (Table of Contents)

    • Abstract
    • Ⅰ. INTRODUCTION
    • Ⅱ. RELATED WORKS
    • Ⅲ. SMART TOURISM DATA MINING TECHNOLOGY COMBINED WITH APRIORI ASSOCIATION RULE MINING
    • Ⅳ. EFFECTIVENESS ANALYSIS OF DATA MINING TECHNOLOGY FOR SMART TOURISM COMBINED WITH APRIORI
    • Abstract
    • Ⅰ. INTRODUCTION
    • Ⅱ. RELATED WORKS
    • Ⅲ. SMART TOURISM DATA MINING TECHNOLOGY COMBINED WITH APRIORI ASSOCIATION RULE MINING
    • Ⅳ. EFFECTIVENESS ANALYSIS OF DATA MINING TECHNOLOGY FOR SMART TOURISM COMBINED WITH APRIORI
    • Ⅴ. CONCLUSION
    • REFERENCES
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