A density-based approach for querying informative constraints for clustering

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. (c) 2020 Elsevier Ltd. All rights reserved.