Study on Semi Supervised Clustering by Metric Learning
Semi Supervised methods use a small amount of auxiliary information as a guide in the learning process in presence of unlabeled data.When using a clustering algorithm,the auxiliary information has the form of side information,that is a list of co-clustered points.The use of Semi Supervised methods may be useful especially in very difficult tasks,such as biological experiments.There are two frequently methods in Semi Supervised clustering:one is constraint-based methods that guide the clustering algorithm towards a better grouping of the data,the other is distance-based learning .In this paper,how to develop new metric learning fuzzy clustering-based is importantly discussed here.
semi supervised learning fuzzy clustering labeled data metric learning data sets
Xiuqin Jiang Shitong Wang
School of Information Technology,Jiangnan University,Wuxi,Jiangsu,China
国际会议
大连
英文
634-638
2008-07-27（万方平台首次上网日期，不代表论文的发表时间）