关键词: 属性值细化, 增量学习, 属性约简, 粗糙集, 知识粒度

Abstract: In practices, many real data in databases may vary dynamically. One has to run a knowledge acquisition method repeatedly in order to acquire new knowledge. This is very time-consuming. To overcome this deficiency, incremental approaches have been presented to deal with dynamic data set. This paper proposes a matrix-based incremental reduction approach with attribute values refining. When a part of data in a given data set is replaced by some new data, compared with the non-incremental reduction approach, the developed incremental reduction approach can find a new reduct in a much shorter time. Finally, experiments on two data sets downloaded from UCI show that the developed algorithm is effective.

Key words: attribute values refining, incremental learning, attribute reduction, rough set, knowledge granularity