Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space by Teng Qiu and Yongjie Li.
Abstract:
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.
The latest in a series of papers exploring clustering issues. The author’s concede the method demonstrated here isn’t important but represents another step in their exploration.
It isn’t often that I see anything other than final and “defend to the death” results. Preliminary and non-successful results being published will increase the bulk of scientific material to be searched but it will also leave a more accurate record of the scientific process.
Enjoy!