关键词: 改进Apriori算法,  数据挖掘, 样本熵, 极限学习机

Abstract: Drug information data has the characteristic of imbalanced categories, with poor interpretability and a large number of sensitive data. The application effect and mining accuracy of sensitive data are low. Therefore, an improved Apriori based sensitive data mining algorithm for drug information is proposed. The drug data is decomposed into several band limited intrinsic mode functions, and is updated and denoised, the feature subset of the sensitive data is extracted according to the information gain of the feature subset of the drug sensitive data and the Monte Carlo sampling strategy. The relationship between the hidden layer output function and the feature subset is analyzed. The extreme learning machine is introduced to improve the Apriori algorithm. And the drug combinations with significant relevance are screened out and solved. The sensitive data features are matched corresponding to the candidate feature subset and a sensitive data mining function is constructed. The experimental results show that the data signal fluctuation amplitude is small, and sensitive data can be clearly distinguished. The number of erroneous data mined does not exceed 2, improving the interpretability of sensitive data.

Key words: improved Apriori algorithm, data mining, sample entropy, extreme learning machine