An algorithm for rules acquisition from the incomplete decision table is proposed, which uses the attribute
importance measure based on discernibility. This algorithm uses a method by specialization, in which condition attributes
are considered to be added to selected attributes set in order of discernibility until the selected attributes set can make the
classification. Certain rules are extracted from the consistent blocks with the single generalized decision, and the uncertain
rules are extracted from other consistent blocks. An attribute necessity judgment step is constructed to remove redundant
attributes of each rule. Besides, a rule reduction procedure is also constructed, which helps to enhance the rule generalization
ability. The experiments and comparison show that the proposed algorithm can get the simple and effective rules.