Semi-Supervised Fuzzy Clustering with Feature Discrimination

Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to obtain a single set of features or weights by weighting and discriminating the information provided by the user. By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. Experiments on several standard benchmark data sets demonstrate the effectiveness of the proposed method.半监督聚类算法正日益被用于从带有部分标记模式的数据中挖掘隐藏的结构信息。为使聚类方法具备实用性并为用户所接受，所提供的信息需简洁自然、数量可控。为提升识别能力，我们针对完整数据集采用高效的特征增强流程，通过对用户提供的信息进行加权与判别，得到单一的特征集或权重集合。通过引入成对约束，我们提出了一种具备特征判别能力的半监督模糊聚类算法（SFFD），该算法集成了全自适应距离函数。在多个标准基准数据集上开展的实验验证了所提方法的有效性。