Clustering of nonstationary data streams: a survey of fuzzy partitional methods

YesData streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift.Ministero dell‘Istruzione, dell‘Universitá e della Ricerca

Evolving Gustafson-kessel Possibilistic c-Means Clustering

AbstractThis 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 ap- pealing 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