A Novel Attribute Reduction Algorithm for Incomplete Information Systems Based on a Binary Similarity Matrix

With databases growing at an unrelenting rate, it may be difficult and complex to extract statistics by accessing all of the data in many practical problems. Attribute reduction, as effective method remove redundant attributes from massive data, has demonstrated its remarkable capability simplifying information systems. In this paper, we concentrate on reducing incomplete We introduce a novel definition binary similarity matrix present calculate significance correspondence. Secondly, develop heuristic attribute reduction algorithm using knowledge. addition, use numerical example showcase practicality accuracy algorithm. conclusion, demonstrate through comparative analysis that our outperforms some existing methods.