Integrating Statistical Significance and Discriminative Power in Pattern
  Discovery
Leonardo Alexandre Rafael S. Costa Rui Henriques
Pattern discovery plays a central role in both descriptive and predictive
tasks across multiple domains. Actionable patterns must meet rigorous
statistical significance criteria and, in the presence of target variables,
further uphold discriminative power. Our work addresses the underexplored area
of guiding pattern discovery by integrating statistical significance and
discriminative power criteria into state-of-the-art algorithms while preserving
pattern quality. We also address how pattern quality thresholds, imposed by
some algorithms, can be rectified to accommodate these additional criteria. To
test the proposed methodology, we select the triclustering task as the guiding
pattern discovery case and extend well-known greedy and multi-objective
optimization triclustering algorithms, $\delta$-Trimax and TriGen, that use
various pattern quality criteria, such as Mean Squared Residual (MSR), Least
Squared Lines (LSL), and Multi Slope Measure (MSL). Results from three case
studies show the role of the proposed methodology in discovering patterns with
pronounced improvements of discriminative power and statistical significance
without quality deterioration, highlighting its importance in supervisedly
guiding the search. Although the proposed methodology is motivated over
multivariate time series data, it can be straightforwardly extended to pattern
discovery tasks involving multivariate, N-way (N>3), transactional, and
sequential data structures.
  Availability: The code is freely available at
https://github.com/JupitersMight/MOF_Triclustering under the MIT license.