To solve the problem,we proposed a framework for semi-supervised clustering.
为解决此问题,提出了一种新的半监督聚类算法框架。
参考来源 - 一种用于处理高维稀疏数据的半监督聚类算法·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
Experimental result demonstrates that compared with previously proposed semi-supervised clustering algorithm this method produces better clusters.
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。
The pairwise constraints are the most common prior knowledge, and many semi-supervised clustering algorithms are based on the type of constraints.
成对约束是先验知识中最普遍的，目前许多半监督聚类算法都基于此类约束形式。
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