Confidence: Indicates the probability

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the occurrence of the latter item when the former item is included. Lift: Indicates the degree of improvement in the probability of the latter item occurring relative to the general probability of the latter item occurring when the former item is included. Common association analysis algorithms Apriori algorithm: A classic association rule mining algorithm that discovers frequent item sets by searching layer by layer. FP-growth algorithm.






An algorithm based on frequent pattern trees with higher efficiency. FP-growth+: An improved Email List version of the FP-growth algorithm that supports mining more complex patterns. Advantages and Disadvantages of Association Analysis Advantages: Easy to understand and implement Discover implicit relationships in data Can be used in a variety of fields Disadvantages: High data quality requirements Easy to generate a large number of rules, which need to be screened Not suitable for continuous data Challenges of Association Analysis Data sparsity: There are a large number of infrequent item sets in the data set. Dimensional disaster: When the data dimension is high, the amount of calculation will increase dramatically. Noisy data.







The presence of noise in the data will affect the mining results. Summary Association analysis is a powerful data mining technique that can help us discover valuable information from massive data. However, in practical applications, we need to pay attention to its limitations and combine it with other data mining techniques to solve complex problems. If you want to know more about association analysis, you can ask the following questions: What is the difference between association analysis and classification? How to choose a suitable association analysis algorithm? How to evaluate the results of association analysis? What are the application cases of association analysis in actual projects? I will answer you wholeheartedly!