To overcome the disadvantage of those attribute reduction algorithms based on the discernibility matrix with high
space complexity, a compact storage structure called condensing tree(C-Tree) and corresponding efficient algorithms for
attribute reduction are introduced in the existing reference, respectively. However, the mentioned algorithms in the reference
only consider the case of the static decision table. Therefore, an incremental updating algorithm is proposed for attribute
reduction based on C-Tree in the case of inserting, which only needs to modify the related nodes in the corresponding paths
when updating the C-Tree. After dynamically computing a core, attribute reduction can be effectively updated by utilizing
the old attribute reduction. Theoretical analysis and experiments show that the proposed algorithm is effective and feasible.