Title: Study on high-dimensional biomedical data mining method based on K-means clustering algorithm

Authors: Octavia Panum

Addresses: College of Engineering, University of Miami, Coral Gables, FL 33124, USA

Abstract: To address the issue of incomplete data mining caused by missing values, this study proposes a high-dimensional biomedical data mining method based on the K-means clustering algorithm. The methodology comprises three main steps: First, missing data values are imputed using the FCMSI algorithm. Second, linear discriminant analysis is employed for feature extraction, where generalised eigenvalues are calculated based on the inverse matrix of the intra-class divergence matrix, enabling dimensionality reduction through projection. Finally, the K-means algorithm is applied for data mining, incorporating probability weights to reselect clustering centres and avoid local optima. Experimental results demonstrate that the proposed method achieves lower mean square error and logarithmic loss while producing more comprehensive data mining outcomes.

Keywords: K-means clustering algorithm; data mining; FCMSI algorithm; linear discriminant analysis; missing value filling.

DOI: 10.1504/IJCAT.2026.154042

International Journal of Computer Applications in Technology, 2026 Vol.78 No.4, pp.281 - 287

Received: 18 Nov 2024
Accepted: 06 May 2025
Published online: 10 Jun 2026 *

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