This talk presents the development, analysis, and testing of a knowledge discovery framework and methodology called Iterative Denoising. This methodology is well-suited for the analysis of high-dimensional data from a variety of application domains. We discuss the motivation, design, and experimental results of Iterative Denoising on multiple datasets, and discuss its suitability for the bioinformatics domain.