Abstract: On line analytical processing （OLAP） usually involves complex queries on very large database Pre aggregation is frequently used to speed up the query response time Storage estimation should be done in advance for selective pre aggregation The solutions of the problem boil down to two categories: one is based on probabilistic counting and mathematical approximation The other one based on a priori distribution model is to extrapolate the estimated parameters of distribution on sampling subset to the whole dataset A novel approach named FSC （frequent sets counting） is presented for view size estimation based on the frequent sets mining and can derive estimation of all views in a cube by two scans of database The results indicate that the proposed scheme approximates more accurately than other schemes, especially for high skewed dataset