Rough Set Analysis for Categorizing Motorcycles based on 100 Cubic Centimeters (CC) Engine Displacements

Abstract

Rough Set Theory (RST) is a mathematical approach used for dealing with uncertainty and vagueness in decision-making and data analysis. It provides a framework for classifying objects into different equivalence classes based on their attributes or characteristics. In RST, the concept of different  bikes can be analyzed based on their attributes or characteristics. Each bike can be represented as an object with a set of attributes such as engine displacement, weight, top speed, fuel efficiency and price. Another class may consist of bikes with lower engine displacement, lighter weight, and better fuel efficiency, which could be more suitable for daily commuting. By applying RST, we can analyze the relationship between these attributes and determine the essential and non-essential features of 100  bikes. This analysis can help in decision making processes, such as choosing the right bike based on specific requirements or preferences. It's important to note that the application of RST to100  bikes is just one example of how this mathematical approach can be used in decision-making and data analysis. The specific attributes and classes may vary depending on the context and purpure of the analysis.