Current spectral clustering algorithms suffer from the sensitivity to existing noise and parameter scaling and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real-world datasets with heterogeneous densities. This article aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically models the heat diffusion traces along the entire time scale, so it ensures robustness during the clustering process, while LDAT probabilistically reveals the local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, it not only provides an advanced noise-resisting and density-aware spectral mapping to the original dataset but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithm outperforms state-of-the-art clustering algorithms for the data with heterogeneous density distributions and achieves robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Aggregated heat kernel
- Local density affinity transformation