## 1 Introduction  
Data dimensionality has emerged as a critical challenge in modern machine learning and data analytics, particularly as organizations collect increasingly complex, high-dimensional datasets across various domains. The exponential growth in data volume and feature count often leads to computational inefficiency, overfitting, and diminished model performance--a phenomenon commonly referred to as the \"curse of dimensionality\". Effective dimensionality reduction techniques have therefore become essential components in data preprocessing pipelines, enabling analysts to identify relevant patterns while eliminating redundant or noisy information. This paper introduces a novel hybrid approach that addresses these challenges by seamlessly integrating unsupervised clustering-based feature extraction with supervised feature selection methods, offering a framework for handling high-dimensional data without compromising analytical accuracy or interpretability. Data reduction techniques are often utilized in data analysis to improve computational efficiency and mitigate the \"curse of dimensionality\", which occurs when the number of features becomes excessively large [1; 2]. Two primary approaches for data reduction are feature selection and feature extraction. Feature selection identifies a subset of the most relevant features, while feature extraction creates a new set of features by transforming the existing ones [3]. This work proposes a novel approach that combines feature extraction through clustering and feature selection for data reduction in unsupervised datasets for high-dimensional data. Feature extraction is achieved through K-means clustering, where cluster membership serves as a new feature [1; 4; 5; 6; 7; 8]. Subsequently, the K-Nearest Neighbors (KNN) and Random Forest (RF), a supervised learning technique, were employed for feature selection. This hybrid approach leverages the strengths of unsupervised learning (clustering) to capture inherent data structure and the strength of supervised learning (KNN and RF) to identify the most relevant features, ultimately enhancing model performance. Clustering is an unsupervised machine-learning technique that groups data points into clusters based on similarity. Common clustering algorithms include centroid-based (e.g., K-means), density-based (e.g., DBSCAN), and hierarchical clustering approaches [9; 10; 11; 12]. The K-means algorithm is popular due to its simplicity and efficiency. There are several variations of K-means [13], which primarily differ in how they initialize or update the centroids (cluster centers). This work focuses on the standard K-means approach, where the K centroids are randomly selected from the N data points in the dataset. The K-means algorithm iteratively assigns data points to K pre-defined clusters based on their proximity to the cluster centroids (mean of the points within the cluster). The process involves initialization (randomly placing K centroids), data point assignment (assigning each data point to the closest centroid), centroid update (recalculating the centroid based on the assigned points), and convergence criteria (stopping when centroids no longer significantly change) [14; 15; 16; 17; 18; 19]. Recent research explores the potential of K-means clustering for data reduction by leveraging cluster membership as a new feature [20; 21]. This approach captures the inherent structure of the data within the cluster membership, potentially leading to improved model performance. However, interpretability limitations exist, as cluster labels might not directly correspond to the underlying data characteristics [22; 23]. Feature selection aims to identify a subset of the most relevant features from the original data. This improves model performance by reducing overfitting, enhancing interpretability, and reducing computational complexity [3].