Data Mining Management System Optimization using Swarm Intelligence

Because of a phenomenon known as the “curs e of dimensionality,” standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases; however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation.