  The field of scientometrics has shown the power of citation-based clusters
for literature analysis, yet this technique has barely been used for
information retrieval tasks. This work evaluates the performance of citation
based-clusters for information retrieval tasks. We simulated a search process
using these clusters with a tree hierarchy of clusters and a cluster selection
algorithm. We evaluated the task of finding the relevant documents for 25
systematic reviews. Our evaluation considered several trade-offs between recall
and precision for the cluster selection, and we also replicated the Boolean
queries self-reported by the systematic review to serve as a reference. We
found that citation-based clusters search performance is highly variable and
unpredictable, that it works best for users that prefer recall over precision
at a ratio between 2 and 8, and that when used along with query-based search
they complement each other, including finding new relevant documents.
