PCM and APCM Revisited: An Uncertainty Perspective
This work addresses clustering uncertainty in fuzzy algorithms for researchers in pattern recognition, but it is incremental as it builds on existing PCM methods.
The paper revisits possibilistic c-means (PCM) and adaptive PCM (APCM) clustering algorithms by introducing an uncertainty perspective, which provides greater flexibility and control over the clustering process through parameters σ_v and α, unifying their main features into a new framework called UPCM.
In this paper, we take a new look at the possibilistic c-means (PCM) and adaptive PCM (APCM) clustering algorithms from the perspective of uncertainty. This new perspective offers us insights into the clustering process, and also provides us greater degree of flexibility. We analyze the clustering behavior of PCM-based algorithms and introduce parameters $σ_v$ and $α$ to characterize uncertainty of estimated bandwidth and noise level of the dataset respectively. Then uncertainty (fuzziness) of membership values caused by uncertainty of the estimated bandwidth parameter is modeled by a conditional fuzzy set, which is a new formulation of the type-2 fuzzy set. Experiments show that parameters $σ_v$ and $α$ make the clustering process more easy to control, and main features of PCM and APCM are unified in this new clustering framework (UPCM). More specifically, UPCM reduces to PCM when we set a small $α$ or a large $σ_v$, and UPCM reduces to APCM when clusters are confined in their physical clusters and possible cluster elimination are ensured. Finally we present further researches of this paper.