Peak-Padding: Clustering by Padding Density Peaks With the Minimum Padding Cost.

Abstract

Clustering complex-shaped clusters is still chal lenging for most existing clustering algorithms. Herein, the peak-padding clustering algorithm (PeakPad)-clustering by padding density peaks with the minimum padding cost-is proposed. PeakPad executes clustering on the density surface and views complex-shaped clusters as combinations of highly associated single-peak clusters. The minimum padding cost that fully considers the surrounding context of a density peak is proposed to reflect a density peak's center potential, enabling PeakPad to have robust center detection performance. Unlike mean-shift (MSC), which detects centers based on their attributes in a complex-shaped density surface embedded in the high-dimensional space of density and features, PeakPad detects centers in a standard-shaped surface embedded in the 2-D density-change (DC) density space (composed of density and DC feature). Such standardization allows PeakPad to have fast and robust cluster center detection performance on complex-shaped clusters based on the minimum padding cost. Besides, PeakPad can provide a reasonable evaluation of the association between single-peak clusters by using the minimum padding cost. As a result, PeakPad can fast capture complex-shaped clusters, achieve robust center detection performance, and be suitable for large datasets. Benchmark test results on both synthetic and real datasets demonstrate the effectiveness of PeakPad.