The article presents a novel approach to instance selection, employing a two-step algorithm. Initially, the data is clustered using k-means or k-medoids to determine the optimal number of centers. Subsequently, the n-nearest neighbors to these centers are identified and used as data instances. The method aims to investigate the effect of changing the parameter n on the reduced set size and the performance of machine learning (ML) model in classification tasks. Calinski-Harabasz, Davies-Bouldin, and Silhouettes measures are utilized to find the optimal number of centers. Evaluation is carried out on 26 UCI repository datasets by comparing the ML model classification accuracy on the reduced data with that on the original dataset. The level of data reduction, influenced by clustering methods, center determination approaches, and n parameter changes, is assessed. The results indicate a significant reduction in the volume of the dataset and an improved classification accuracy of the ML model compared to the full datasets and the literature benchmarks.