Abstract:

Firstly, a new algorithm based on attribute frequency in the discernibility matrix is used to get the coreattribute of attribute reduction. Secondly, considering the effect of different kinds of marine environment factors, an effective prediction model is established to confirm the coreattribute to be the high effect factors of purse seine outputs. This method addresses the issue by automatically filling vacant item of the fishery monitor data set, and then to take a attribute deduction using the discernibility matrix to get the coreattribute to be the high effect factors of purse seine. The experiment results show that the algorithm efficiently improves sparsity of date set , and promises to make prediction more accurately .

Key words: rough set theory;purse seine;data mining;fishery prediction