Abstract: Multi-label k Nearest Neighbor（ML-kNN） algorithm is a lazy learning approach and has successfully been developed in real application. With the increasing amount of information, it is necessary for the ML-kNN algorithm to be applied to large data sets. This paper firstly conducts clustering algorithm to separate the dataset into several parts, and then, each of which conducts ML-kNN classification. And a series of experiments are carried out on four different datasets. The experimental results show that ML-kNN algorithm proposed works well in terms of accuracy and efficient.

Key words: multi-label classification, Multi-Label k Nearest Neighbor（ML-kNN）, cluster, big data set

摘要： k近邻多标签算法（ML-kNN）是一种懒惰学习算法，并已经成功地应用到实际生活中。随着信息量的不断增大，将ML-kNN算法运用到大数据集上已是形势所需。利用聚类算法将数据集分为几个不同的部分，然后在每一个部分中使用ML-kNN算法，并在四个规模不同的数据集上进行了一系列实验。实验结果表明，基于此思想的ML-kNN算法不论在精度、性能还是效率上都略胜一筹。

关键词: 多标签分类, ML-kNN算法, 聚类, 大数据集