  Clustering analysis has become a ubiquitous information retrieval tool in a
wide range of domains, but a more automatic framework is still lacking. Though
internal metrics are the key players towards a successful retrieval of
clusters, their effectiveness on real-world datasets remains not fully
understood, mainly because of their unrealistic assumptions underlying
datasets. We hypothesized that capturing {\it traces of information gain}
between increasingly complex clustering retrievals---{\it InfoGuide}---enables
an automatic clustering analysis with improved clustering retrievals. We
validated the {\it InfoGuide} hypothesis by capturing the traces of information
gain using the Kolmogorov-Smirnov statistic and comparing the clusters
retrieved by {\it InfoGuide} against those retrieved by other commonly used
internal metrics in artificially-generated, benchmarks, and real-world
datasets. Our results suggested that {\it InfoGuide} can enable a more
automatic clustering analysis and may be more suitable for retrieving clusters
in real-world datasets displaying nontrivial statistical properties.
