关键词: 多粒度粗糙集, 属性约简, 二进制, 加权Apriori算法

Abstract: In the era of big data, it has become increasingly difficult to obtain the data. And data mining is the key technology to solve this problem at present. Apriori algorithm is a common algorithm in data mining by mining potential association rules behind the data. Considering the problems of traditional Apriori algorithm, such as frequent scan data and cumbersome acquisition of candidate items, a weighted Apriori algorithm is proposed to record the number of repetitions of the total number of records. The repetition times are taken as the weight and compression matrix of data sets. Binary Boolean matrix is used to replace the original data set, through the matrix of “AND operation” to obtain the maximum frequent item set to reduce the time complexity. Considering the redundancy of the original data and the inaccuracy of attribute reduction, an algorithm of attribute reduction based on multi-granularity rough set is proposed before the association rules are extracted. The uncertainty of the information is described by the granularity of knowledge, and the attribute value is refined to reduce the precision and reduce the space complexity. Finally, the proposed algorithm is compared with the Apriori algorithm based on frequent matrices and the original Apriori algorithm to verify its practicability and validity.

Key words: multi-granularity rough set, attribute reduction, binary, weighted Apriori algorithm