半监督鲁棒联机聚类算法

将监督信息引入到聚类算法中去,在先前提出的鲁棒联机聚类算法(ROC)的基础上,通过引入以样本类标号形式给出的监督信息,提出了一种半监督的鲁棒联机聚类算法(Semi-ROC).在算法的聚类精度和鲁棒性能上,算法Semi-ROC比ROC和AddC有着更好的性能,在人工数据集和UCI标准数据集上的实验结果表明,Semi-ROC能有效地利用少量的监督信息来提高算法的聚类性能,得到较优的结果.另外,在添加噪声的情况下,算法Semi-ROC比原始的联机聚类算法AddC和ROC都更加鲁棒.

Semi-Supervised Robust On-Line Clustering Algorithm

Recently,a semi-supervised learning has attracted much attention in machine learning community.One reason is that in many learning tasks,there is a large supply of unlabeled data but insufficient labeled data because the latter is much more expensive to obtain than the former.Typically,semi-supervised learning is applicable to both clustering and classification.This paper focuses its attention on semi-supervised clustering.In semi-supervised clustering,some label level or instance level supervised information is used along with the unlabeled data in order to obtain a better clustering result.A semi-supervised robust on-line clustering algorithm called Semi-ROC is developed,which introduces supervision information in the form of class labels into the previously proposed robust on-line clustering(ROC).After introducing the supervised information,the algorithm can get a more confidential result than the ROC and AddC.The experimental results on the artificial dataset and UCI benchmark data sets show that the proposed Semi-ROC can effectively use little supervision information to enhance the clustering performance,the clustering validity can be improved significantly.Besides,when dealing with noises,Semi-ROC is more robust than both ROC and AddC.