Abstract
Dealing with uncertain data requires effective methods to properly describe their real meaning in terms of a tradeoff between interpretability and generality on the process of knowledge formation based on data abstraction. This article proposes an online granulation process based on evolving ellipsoidal fuzzy information granules (EEFIG) and the principle of justifiable granularity (PJG) for data streams parameterization. The granulation process consists in the information granule development taking into consideration the data stream with a simplified optimal granularity allocation. In the sequel, an evolving Takagi–Sugeno fuzzy model based on the ellipsoidal granules is proposed for data reconstruction and one-step ahead prediction from past data numerical evidence. Experimental studies concerning clustering, data granulation, and time-series forecasting are performed to illustrate the effectiveness of the proposed method.
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