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Clustering analysis has become a ubiquitous information retrieval tool in awide range of domains, but a more automatic framework is still lacking. Thoughinternal metrics are the key players towards a successful retrieval ofclusters, their effectiveness on real-world datasets remains not fullyunderstood, mainly because of their unrealistic assumptions underlyingdatasets. We hypothesized that capturing {\it traces of information gain}between increasingly complex clustering retrievals---{\it InfoGuide}---enablesan automatic clustering analysis with improved clustering retrievals. Wevalidated the {\it InfoGuide} hypothesis by capturing the traces of informationgain using the Kolmogorov-Smirnov statistic and comparing the clustersretrieved by {\it InfoGuide} against those retrieved by other commonly usedinternal metrics in artificially-generated, benchmarks, and real-worlddatasets. Our results suggested that {\it InfoGuide} can enable a moreautomatic clustering analysis and may be more suitable for retrieving clustersin real-world datasets displaying nontrivial statistical properties.

Towards Automatic Clustering Analysis using Traces of Information Gain: The InfoGuide Method