Spectral data self-organization based on bootstrapping and clustering approaches

This study introduces a novel technique for self-organizing data, without any prior knowledge on their statistical distribution, fusing efficient strategies from clustering and resampling. The proposed methodology aims at searching for hidden characteristics within the processed dataset and revealing additional data structures or subclasses that can be utilized for identifying irregular groups that are of particular importance in disease modeling. The performance evaluation of the presented algorithm to biomedical data from cervical cancer is tested and analyzed on sample vectors representing the temporal response of tissue areas obtained through multispectral imaging. The results of this study show that stratified, repeated applications of simple clustering schemes can effectively organize big data, giving rise to the application of the proposed method for tissue classification for enabling accurate and early disease diagnosis.