实验表明，该算法较之于已提出的半监督聚类算法，获得了更好的聚类性能。
Experimental result demonstrates that compared with previously proposed semi-supervised clustering algorithm this method produces better clusters.
半监督聚类通过利用少量有标号样本或成对约束等监督信息来提高聚类性能。
Semi-supervised clustering algorithms use a small amount of supervision information in the form of labeled data or pairwise constraints to improve clustering performance.
现有的半监督聚类方法较少利用数据集空间结构信息，限制了聚类算法的性能。
Most of the existing semi-supervised clustering methods neglect the structural information of the data, while the few constraints available may degrade the performance of the algorithms.
相比于无监督聚类分析，半监督聚类利用提供的少量监督信息协助指导聚类过程。
Compared to unsupervised clustering, semi-supervised clustering utilizes a small amount of given prior knowledge to guide the clustering process.
相比于无监督聚类分析，半监督聚类利用提供的少量监督信息协助指导聚类过程。
Compared to unsupervised clustering, semi-supervised clustering utilizes a small amount of given prior knowledge to guide the clustering process.
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