A density-based enhancement to dominant sets clustering
Although there is no shortage of clustering algorithms, existing algorithms are often afflicted by problems of one kind or another. Dominant sets clustering is a graph-theoretic approach to clustering and exhibits significant potential in various applications. However, the authors' work indicates that this approach suffers from two major problems, namely over-segmentation tendency and sensitiveness to distance measures. In order to overcome these two problems, the authors present a density-based enhancement to dominant sets clustering where a cluster merging step is used to fuse adjacent clusters close enough from the original dominant sets clustering. Experiments on various datasets validate the effectiveness of the proposed method.
density-based enhancement dominant sets clustering graph-theoretic approach over-segmentation tendency cluster merging step graph theory pattern clustering set theory unsupervised learning unsupervised learning tools adjacent cluster fusion distance measure sensitiveness