关键词: 多示例学习, K-means聚类, 包间距, 聚类有效性评价

Abstract: Multi-instance learning is a new machine learning framework following supervised learning, unsupervised learning and reinforcement learning. Multi-instance learning and unsupervised learning are combined. This paper proposes a new multi-instance clustering algorithm MI_K-means based on traditional unsupervised learning algorithm K-means. The algorithm MI_K-means adopts mixed Hausdorff distance as similar measure to carry out clustering. Experimental shows that MI_K-means can effectively reveal inherent structure of a multi-instance data set, and it can get better clustering effect than K-means algorithm.

Key words: multi-instance learning, K-means clustering, distance between bags, validity measure on clustering