Abstract: In the traditional single-label mining technology research,each sample belongs to only one label and the labels are mutually exclusive.In the multi-label learning problem,one sample may correspond to multiple labels,and each label is often asso-ciated with each other.At present,the research on the correlation between tags gradually becomes a hot issue in multi-label lear-ning research.Firstly,in order to adapt to the big data environment,the traditional association rule mining algorithm Apriori is parallelized and improved.The Hadoop-based parallelization algorithm Apriori_ING is proposed to realize the generation of the candidate set,the pruning and the support number statistics,and the parallelization.The advantage is that the frequent itemsets and association rules obtained by the Apriori_ING algorithm generate tag sets,and the inference engine based tag set generation algorithm IETG is proposed.Then,the label set is applied to multi-label learning,and a multi-label learning algorithm FreLP is proposed.FreLP uses association rules to generate a set of labels,decomposes the original set of labels into multiple subsets,and then uses the LP algorithm to train the classifier.FreLP was compared with the existing multi-label learning algorithms.Experiment results show that the proposed algorithm can obtain better results under different evaluation indicators.

Key words: Apriori, Association rule, Hadoop, LP, Multi-label learning