Optimized K-Means Clustering for Enhanced Regression Performance

Abstract

In this study, we explore the novel application of clustering techniques in the context of regression analysis. Regression is a key method in data science aimed at predicting the value of real-valued functions for arbitrary input variables. The central hypothesis of our research is that clustering can be leveraged to identify subregions of the input domain where the target function exhibits greater regularity and stability, thus enabling more precise and reliable regression. Specifically, we propose a modified K-means clustering algorithm that optimizes clusters based not only on proximity in feature space but also on the homogeneity of the function values within each cluster, measured via reduced standard deviation. In this paper, we present a series of experimental evaluations analyzing the efficiency of the proposed method. These evaluations also include comparisons with standard k-means clustering and nearest neighbourhood regression methods. The results demonstrate that the proposed method not only reduces the standard deviation within clusters but also improves the regression accuracy.

Received: 05 July 2025 / Accepted: 30 August 2025 / Published: 25 September 2025