The rough logic defined on neighbor-valued decision tables and its truth values of the formulas are discussed in this paper. It is more general than the decision logic defined by Pawlak in the applications of data reduction. At present, the methods used are often Pawlak's data analysis and Skowron's discernible matrix methods. The former is informal, no ease mechanization. The latter is intuitive, easy to understand, but it requires to generate a medial link of discernible matrix, to make unnecessary expenses on time and space. Therefore, in the paper, one side extracts the attributes of attribute neighbor-valued discernible from the neighbor-valued decision table and discernible Conjunctive Normal Form is constructed. The other side simplifies the formula to use absorbable laws and other calculus of logical formulas. It obtains directly all reductions in the neighbor-valued decision table. Since it doesn't need to generate the medial link of discernible matrix, so it can spare space and time, and raise the efficiency of the program run. Thus, reduction of the tables is handled to possess 6 attributes (4 conditional attributes and 2 decision attributes) and 102 objects to use two methods respectively, and to obtain the same results. It uses one side to extract formulas from the tables, and the other side to reduce the formulas in DELPHI 3.0 on PⅠⅠ 233/64 M. The time of program running is about 1 minute 54 seconds; while time of spending is about 1 minute 55 seconds to use the discernible matrix method. Due to the increase of an array (discernible matrix), its space degree of complexity is O(m×n²), where m is the number of attributes, n is the number of objects. So, the space and time occupied will also increase rapidly along with the increment of attributes and objects. The strong points and shortcomings of two methods are quite clear from space and time used.