Semi-supervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular k-means clustering algorithm. As such, they are based on a criterion to minimize. Amongst existing semi-supervised clusterings, Semi-supervised Evidential Clustering (SECM) deals with the problem of uncertain/imprecise labels and creates a credal partition. In this work, a new heuristic algorithm, called SECM-h, is presented. The proposed algorithm relaxes the constraints of SECM in such a way that the optimization problem is solved using the Lagrangian method. Experimental results show that the proposed algorithm largely improves execution time while accuracy is maintained.

半监督聚类是一种受约束的聚类技术，它借助表示为约束的领域知识将未标记数据的集合组织到同质子组中。大多数情况下，这些方法是流行的k均值的变体聚类算法。因此，它们基于最小化的标准。在现有的半监督聚类中，半监督证据聚类（SECM）处理标签不确定/不精确的问题，并创建一个credal分区。在这项工作中，提出了一种新的启发式算法，称为SECM-h。提出的算法通过使用拉格朗日方法解决了优化问题，从而放松了SECM的约束。实验结果表明，该算法在保持精度的同时，大大提高了执行时间。