多标记学习广泛存在于现实生活中，是当今机器学习领域的研究热点.在多标记学习框架中，每个对象由一个示例构成，但可能同时属于多个类别标记，并且各个标记之间相互关联，所以挖掘多标记之间的关联性对于多标记学习框架具有重要的意义.首先对经典的关联规则算法进行改进，提出了基于矩阵分治的频繁项集挖掘算法，并证明了该算法挖掘频繁项集的正确性；进而将该算法应用于多标记学习框架中，分别提出了基于全局关联规则挖掘和局部关联规则挖掘的多标记分类算法；最后对所提出的算法与现有多标记算法进行实验对比，结果表明，算法在5种不同的评价准则下能够取得更好的效果.

In the real world, multi-label learning has become a hotspot in machine learning research area. In the multi-label learning problem, each instance is usually described by multiple class labels, which could be correlated with each other. It is well known that exploiting label correlations is important for multi-label learning. In this paper, an improved association rule mining algorithm based is designed on the matrix divide-and-conquer strategy. In addition, a proof is given to show the proposed algorithm in finding correct frequent items, and an application of the algorithm to the multi-label learning framework is also provided. Moreover, a global association rule mining and a local association rule mining based multi-label classification methods are proposed. Experimental results on several datasets show that the proposed methods can obtain a better classification performance on 5 different evaluation criteria.

国家自然科学基金（61773208，61403200，71671086）；浙江省海洋大数据挖掘与应用重点实验室资助项目（OBDMA201602）