With the potential and advantages of infrared (IR) spectroscopic applications in biological studies, and the introduction of multi-channel focal plane array (FPA) mid-IR detectors, efficient unsupervised clustering algorithms are required to identify and group similar useful spectra from background or outlier spectra within large hyperspectral datasets. Such classification algorithms are crucial for enabling further multivariate analysis. In this paper, a clustering method coined as the improved leader-follower cluster analysis (iLFCA) algorithm is expounded and demonstrated on two mid-IR imaging datasets of exfoliated oral mucosa cells: a Large Array (LA) 64 × 64 pixels image and a Very Large Array (VLA) simulated 128 × 128 pixels image created as a montage of the original LA data. By concatenating the normalized vector form of each spectrum and its integrated areas of characteristic spectral bands, such as Amide I and II, the specificity and efficacy of the clustering algorithm is enhanced. Human intervention for selecting appropriate user-specified parameters and thresholds is also minimized through the development of an automated bisection search algorithm. This resulted in better computational efficiency for iLFCA compared to its predecessor LFCA algorithm. A comparison of iLFCA and LFCA with a common unsupervised classification method based on Principal Component Analysis (PCA) shows iLFCA achieving better clustering results at shorter computational time. In particular, iLFCA has the capability to process larger datasets, namely VLA datasets, which caused both LFCA and PCA-based methods to fail because of computer memory space limitations. iLFCA can potentially be applied to analyze vibrational microspectroscopic data for diagnosis/screening of biological tissue and cells samples, cell culture growth monitoring, and examination of active pharmaceutical ingredients (APIs) distribution and real-time release of pharmaceutical tablets.