Relevance feedback (RFB) involves requesting some user judgments for an initial set of search results and then using these judgments to improve search results. Typical queries may have multiple possible interpretations or facets, only one of which is relevant to a user's need, but top search results may be dominated by one interpretation or facet. Thus, if the user is only given the top results to inspect, none of them may be relevant. One way to solve this is to intentionally diversify the top few results to cover multiple interpretations. This paper proposes the use of density-based clustering for the purpose of results diversification in the context of RFB. Other traditional clustering algorithms are also used for a comparative study. Clustering is compared to a baseline that nominates the top results from an initial ranked list and compared to using the top results after re-ranking using Maximal Marginal Relevance. The results show that density-based clustering achieves the best results with a statistically significant improvement of 12% over the baseline.