Semi-supervised DenPeak Clustering with Pairwise Constraints

Density-based clustering is an important class of approaches to data clustering due to good performance. Among this class of approaches, DenPeak is an effective density-based clustering method that can automatically find the number of clusters and find arbitrary-shape clusters in relative easy scenarios. However, in many situations, it is usually hard for DenPeak to find an appropriate number of clusters without supervision or prior knowledge. In addition, DenPeak often fails to find local structures of each cluster since it assigns only one center to each cluster. To address these problems, we introduce a novel semi-supervised DenPeak clustering (SSDC) method by introducing pairwise constraints or side information to guide the cluster process. These pairwise constraints or side information improve the clustering performance by explicitly indicating the affiliated cluster of data samples in each pair. Concretely, SSDC firstly generates a relatively large number of temporary clusters, and then merges them with the assistance of samples’ pairwise constraints and temporary clusters’ adjacent information. The proposed SSDC can significantly improve the performance of DenPeak. Its superiority to state-of-the-art clustering methods has been empirically demonstrated on both artificial and real data sets.

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