Biomedical data classification using hierarchical clustering

Biomedical spectra, such as those acquired from magnetic resonance (MR) spectrometers, often have the characteristics of high dimensionality and small sample size. These two characteristics make the classification of such spectra difficult. Hierarchical clustering produces robust clustering results, especially when working on small size high-dimensional datasets. The goal of this research is to investigate the effectiveness of hierarchical clustering for the classification of high-dimensional biomedical spectra. The classification results are benchmarked against linear discriminant analysis (LDA).