An incremental algorithm for granular counting with possibility theory

Data counting is non-trivial when data are uncertain. In the case of uncertainty due to incompleteness, possibility theory can be used to define a granular counting model. Two algorithms were proposed in literature to compute granular counting: exact granular counting, with quadratic time complexity, and approximate granular counting, with linear time complexity. However, both algorithms require that all data are available before counting. This paper presents an incremental granular counting algorithm which provides an efficient and exact computation of the granular count without the need of having all data available, thus opening the door to applications involving data streams.