Some membership functions via neighborhood systems: application to a rough set decision making

The neighborhood structure can represent information or knowledge about relationships between a universe's object. In other words, such elements or objects are somewhat  similar  to that element in an element's neighborhood.  Pawlak presented the idea of rough sets as useful tools for learning computer science and information systems. Neighborhood structures used this principle to be generalized and studied. This paper uses a neighborhood method to solve several rough set theory problems.  By using a neighborhood of objects in the information system and illustrative examples to apply it, we introduce some new definitions of attributes, membership function and accuracy measurement.  A decision making of our method gives an accurate decision and helps with decision correlation to calculate the accuracy of each attribute that builds an approach to decision making.