Affiliations: [a] Department of Information and Engineer, Shandong University of Science and Technology, Taian, Shandong, China
| [b] Department of Economic and Management, Shandong University of Science and Technology, Taian, Shandong, China
Correspondence:
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Corresponding author. Xiaoxia Lin, Department of Information and Engineer, Shandong University of Science and Technology, Taian, Shandong, 271019, China. E-mail: [email protected].
Abstract: With the continuous development of internet and information technology, human beings need to process a lot of information and data. When processing a large amount of information, data mining technology must be used. In order to better mine the required data information quickly based on condition matching, an optimized Apriori and FP - Growth association rule mining algorithm is proposed. Based on the algorithm flow and evaluation model, an optimization and up-date scheme is proposed, an effective data transmission evaluation model is established by effectively evaluating the state of data analysis, and the corresponding evaluation results are given. By introducing the idea of improved decomposition database to reduce the collection of infrequent databases, the algorithm adaptability is improved. In order to verify the feasibility and reliability of the method, the case experiment is demonstrated. Based on the experimental results, the algorithm is more effective in actual operation efficiency and data mining precision.
Keywords: Apriori algorithm, FP-Growth algorithm, data minin
