摘要: 多示例学习是继监督学习、非监督学习、强化学习后的又一机器学习框架。将多示例学习和非监督学习结合起来,在传统非监督聚类算法K-means的基础上提出MI_K-means算法,该算法利用混合Hausdorff距离作为相似测度来实现数据聚类。实验表明,该方法能够有效揭示多示例数据集的内在结构,与K-means算法相比具有更好的聚类效果。
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
中图分类号:
谢红薇;李晓亮. 基于多示例的K-means聚类学习算法[J]. 计算机工程, 2009, 35(22): 179-181.
XIE Hong-wei; LI Xiao-liang. K-means Clustering Learning Algorithm Based on Multi-instance[J]. Computer Engineering, 2009, 35(22): 179-181.