关键词: 多示例聚类，推土机距离，k-medoids

Abstract: In the setting of multi-instance learning, each sample is represented by a bag composed of multiple instances.Previous studies on clustering mainly deal with the single instance in traditional learning setting, so it can't be applied to multi instance problem directly. In this paper, based on earth mover's distance, a novel multiplcinstance clustering algothrim named ECMKIL was presented. Firstly we calculated the bag's instances' similarity, emerged the similarity ones, then regarded the two bags' instances as suppliers and consumers, calculated the goods and capacity. To deal with the supplier-consumer imbalance problem, we solved it by multiplying the goods. Finally, used k-medoids to cluster the multi-instance data. Experimental results on MUSK, Corel and SIVAL data set indicate that the ECMKIL method is effective.

Key words: Multi instance clustering,Earth mover's distance,K-medoids