计算机科学与探索Issue(1)：105-111,7.DOI:10.3778/j.issn.1673-9418.1405035
结合特征偏好的半监督聚类学习
Semi-Supervised Clustering Learning Combined with Feature Preferences
摘要
Abstract
Semi-supervised clustering is one of the important research subjects in the machine learning community. It guides semi-supervised clustering by using the label information of a small amount of data or the information of relative preference relations between features. However, the only single-facet information is considered as prior knowledge in existing semi-supervised clustering algorithms. It is relatively rare to jointly use information from two different facets in pattern and feature into semi-supervised clustering. To remedy such shortcoming, based on tradi-tional semi-supervised clustering algorithms, this paper proposes an extended semi-supervised clustering algorithm by jointly exploiting both given feature preferences in feature facet and semi-supervised information of a small amount of data in pattern facet. The experimental results show its effectiveness.关键词
半监督学习/聚类/半监督聚类/特征偏好/标记信息Key words
semi-supervised learning/clustering/semi-supervised clustering/feature preferences/label information分类
信息技术与安全科学引用本文复制引用
方玲,陈松灿..结合特征偏好的半监督聚类学习[J].计算机科学与探索,2015,(1):105-111,7.基金项目
The Natural Science Foundation of Jiangsu Province of China under Grant No. BK2011728(江苏省自然科学基金) （江苏省自然科学基金）
the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20133218110032(高等学校博士学科点专项科研基金) （高等学校博士学科点专项科研基金）
