关键词: 半监督聚类, 成对约束, 主成分分析, 线性判别分析

Abstract: The semi-supervised clustering is to mine and help to understand better the structure of unlabeled data and to more closely conform to the user’s preferences using those supervised data，in comparison with unsupervised clustering.Most existing semi-supervised clustering methods are designed for handling low-dimensional data.In this paper，a novel Semi-supervised Clustering Approach with Discriminant Analysis（SCADA） is presented for clustering the high-dimensional data.Specifically，the data are first mapped onto the low-dimensional space by principal component analysis such that constrained spherical K-means algorithm is used to cluster those transformed data.Secondly，linear discriminant analysis is used to reduce the number of the dimensionality of the data in terms of the clustering results.Finally，the data in the embedded space are clustered.Indeed，the experimental results on several real-world data sets show the SCADA method can effectively deal with the high-dimensional data and provides an appealing clustering performance.

Key words: semi-supervised clustering, pairwise constraint, principal component analysis, linear discriminant analysis