Evolving Gustafson-Kessel Possibilistic c-Means Clustering

This paper presents an idea of evolving Gustafson-Kessel possibilistic c-means clustering (eGKPCM). This approach is extension of well known possiblilistic c-means clustering (PCM) which was proposed to address the drawbacks associated with the constrained membership functions used in fuzzy c-means algorithms (FCM). The idea of possiblistic clustering is appealing when the data samples are highly noisy. The extension to Gustafson-Kessel possibilistic clustering enables us to deal with the clusters of different shapes and the evolving structure enables us to cope with the data structures which vary during the time. The evolving nature of the algorithm makes it also appropriate for dealing with big-data problems. The proposed approach is shown on a simple classification problem of unlabelled data.