During the last years, constrained clustering has emerged as an interesting direction in machine learning research. With constrained clustering, the quality of results can be improved by using constraints if a high-quality set of constraints is selected. Querying beneficial constraints is a challenging task because there is no metric for measuring the quality of constraints before clustering. A new method is proposed in this study that estimates density and impurity of data points on different adjacency distances and calculates centrality for each data point by applying a density tracking approach on the obtained densities. The obtained information is then used to select a set of high-quality constraints. Multi-resolution density analysis to more accurately estimate the point-point relationship of data, data density tracking in order to estimate the impurity and centrality of data, and selection of constraints from skeleton of clusters in order to discover the intrinsic structure of data can be mentioned as the most important contributions of this study. To verify the effectiveness of the proposed method, we conducted a series of experiments on real data sets. The obtained results show that the proposed algorithm can improve the clustering process compare with some recent reference algorithms.

在过去的几年中，约束聚类已经成为机器学习研究中一个有趣的方向。使用约束聚类，如果选择了一组高质量的约束，则可以通过使用约束来提高结果的质量。查询有益约束是一项艰巨的任务，因为在聚类之前没有衡量约束质量的指标。在这项研究中提出了一种新方法，该方法可以估计不同邻接距离上的数据点的密度和杂质，并通过对获得的密度应用密度跟踪方法来计算每个数据点的中心度。然后，将获得的信息用于选择一组高质量约束。多分辨率密度分析可以更准确地估算数据的点对点关系，为了估计数据的杂质和中心性而进行数据密度跟踪，以及从簇的骨架中选择约束条件以发现数据的固有结构，可以作为本研究的最重要贡献。为了验证该方法的有效性，我们对真实数据集进行了一系列实验。所得结果表明，与最近的一些参考算法相比，该算法可以改善聚类过程。