Wavelet-based cluster analysis (WCA) is a technique that can be used to separate fMRI and phMRI data into different clusters, where no model of the neural response is known a priori, based on the similarity of decomposed time courses at certain temporal scales. Here we extend this iteratively as an interactive step in a data-driven analysis. It works by removing voxels from further analysis by examining how they are clustered at temporal scales which may be different to that of any neural response under investigation. This is in contrast to existing techniques that suppress artefactual effects through excessive smoothing which may also suppress localised drug responses in phMRI. The method is demonstrated here on an auditory fMRI experiment. We conclude that it will be a useful step in the preprocessing and further analysis of phMRI data.