Attribute reduction is a key step to discover interesting pattern in decision system with numbers of attributes available. Moreover, data processing tools have been developed rapidly in recent years, and then the information system may increase quickly in attributes with time in real-life applications. How to update attribute reduction efficiently under the attribute generalization becomes an important task in knowledge discovery related tasks. The attribute reduction of information system may alter with the increase of attributes. This paper aims for investigation of incremental attribute reduction algorithm based on knowledge granularity in information system on the variation of attribute set. Incremental mechanisms to calculate the new knowledge granularity are first introduced. Then, the corresponding incremental algorithms are presented for attribute reduction based on the calculated knowledge granularity when multiple attributes are added to a decision table. Finally, experiments performed on UCI data sets and the complexity analysis show that the proposed incremental methods are effective and efficient to update attribute reduction with the increase of attributes.

No abstract

Attribute reduction based on rough set theory has attracted much attention recently. In real‐life applications, many decision tables may vary dynamically with time, e.g., the variation of attributes, objects, and attribute values. The reduction of decision tables may change on the alteration of attribute values. The paper focuses on dynamic maintenance of attribute reduction when varying data values of multiple objects. Incremental mechanisms for knowledge granularity are proposed first, which aims to update attribute reduction effectively. Then, a group incremental reduction algorithm with varying data values is developed. When attribute values of multiple objects have been replaced by new ones in decision table, the proposed incremental algorithm can find the new reduct in a much shorter time. The time complexity analysis and experiments on different data sets from UCI have validated that the proposed incremental algorithms are efficient and effective to update the reduction with the variation of attribute values.

No abstract

No abstract