关键词: 半监督学习, 聚类, 半监督聚类, 特征偏好, 标记信息

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 traditional 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.

Key words: semi-supervised learning, clustering, semi-supervised clustering, feature preferences, label information