Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
This is an incremental improvement for clustering algorithms, addressing a specific bottleneck in graph-based methods.
The paper tackles the problem of clustering by preventing undesired edges at the source, resulting in automatic cluster formation without the need for edge removal.
In our previous works, we proposed a physically-inspired rule to organize the data points into an in-tree (IT) structure, in which some undesired edges are allowed to occur. By removing those undesired or redundant edges, this IT structure is divided into several separate parts, each representing one cluster. In this work, we seek to prevent the undesired edges from arising at the source. Before using the physically-inspired rule, data points are at first organized into a proximity graph which restricts each point to select the optimal directed neighbor just among its neighbors. Consequently, separated in-trees or clusters automatically arise, without redundant edges requiring to be removed.