Rough Set Data Preprocessing: Discretization of Continuous Data

Rough set data preprocessing refers to the preliminary treatment of raw data to facilitate subsequent analysis and processing. A core component of this process involves discretizing continuous data. Discretization is the transformation of continuous numerical values into discrete intervals or categories, which significantly reduces computational complexity and enhances the efficiency of data mining algorithms. Typical discretization methods include equal-width binning (dividing the data range into intervals of equal size) and equal-frequency binning (creating intervals containing approximately the same number of data points). These techniques partition continuous data into distinct categories or intervals, enabling more effective implementation of rough set theory for data analysis and decision-making. Code implementation often involves sorting data, calculating bin boundaries, and mapping values to discrete symbols using functions like pandas.cut() for equal-width or numpy.percentile() for equal-frequency approaches.