By using cardinality and relevance information about a set of attributes and concept hierarchies, a top-down incremental data partitioning method is proposed for quantitative rule derivation from database in parallelism. Based on sequential incremental approach, we proposed two parallel versions of incremental partitioning algorithms. These two parallel algorithms are multidimensional-based to partition data set into multiple independent subsets for further rule derivation process. The second version of the parallel algorithm improves the first in terms of load balance.