Incremental updating of rough approximations in interval-valued information systems under attribute generalization

Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information, which results in the continuous updating of rough approximations for rough set-based data analysis. In this paper, on the basis of the similarity-based rough set model in IvIS, we develop incremental approaches for updating rough approximations in IvIS under attribute generalization, which refers to the dynamic changing of attributes. Firstly, increment relationships between the original rough approximations and the updated ones when adding or deleting an attribute set are analyzed, respectively. And the incremental mechanisms for updating rough approximations in IvIS are introduced, which carry out the computation using the previous results from the original data set along with new results. Then, the corresponding incremental algorithms are designed based on the proposed mechanisms. Finally, comparative experiments on data sets from UCI as well as artificial data sets are conducted, respectively. Experimental results show that the proposed incremental algorithms can effectively reduce the running time for the computation of rough approximations in comparison with the static algorithm.