  Pattern discovery is a machine learning technique that aims to find sets of
items, subsequences, or substructures that are present in a dataset with a
higher frequency value than a manually set threshold. This process helps to
identify recurring patterns or relationships within the data, allowing for
valuable insights and knowledge extraction. In this work, we propose
Information Gained Subgroup Discovery (IGSD), a new SD algorithm for pattern
discovery that combines Information Gain (IG) and Odds Ratio (OR) as a
multi-criteria for pattern selection. The algorithm tries to tackle some
limitations of state-of-the-art SD algorithms like the need for fine-tuning of
key parameters for each dataset, usage of a single pattern search criteria set
by hand, usage of non-overlapping data structures for subgroup space
exploration, and the impossibility to search for patterns by fixing some
relevant dataset variables. Thus, we compare the performance of IGSD with two
state-of-the-art SD algorithms: FSSD and SSD++. Eleven datasets are assessed
using these algorithms. For the performance evaluation, we also propose to
complement standard SD measures with IG, OR, and p-value. Obtained results show
that FSSD and SSD++ algorithms provide less reliable patterns and reduced sets
of patterns than IGSD algorithm for all datasets considered. Additionally, IGSD
provides better OR values than FSSD and SSD++, stating a higher dependence
between patterns and targets. Moreover, patterns obtained for one of the
datasets used, have been validated by a group of domain experts. Thus, patterns
provided by IGSD show better agreement with experts than patterns obtained by
FSSD and SSD++ algorithms. These results demonstrate the suitability of the
IGSD as a method for pattern discovery and suggest that the inclusion of
non-standard SD metrics allows to better evaluate discovered patterns.
