Scaling pattern mining through non-overlapping variable partitioning

Abstract: Biclustering algorithms play a central role in the biotechnological and biomedical domains. The knowledge extracted supports the extraction of putative regulatory modules, essential to understanding diseases, aiding therapy research, and advancing biological knowledge. However, given the NP-hard nature of the biclustering task, algorithms with optimality guarantees tend to scale poorly in the presence of high-dimensionality data. To this end, we propose a pipeline for clustering-based vertical partitioning that takes into consideration both parallelization and cross-partition pattern merging needs. Given a specific type of pattern coherence, these clusters are built based on the likelihood that variables form those patterns. Subsequently, the extracted patterns per cluster are then merged together into a final set of closed patterns. This approach is evaluated using five published datasets. Results show that in some of the tested data, execution times yield statistically significant improvements when variables are clustered together based on the likelihood to form specific types of patterns, as opposed to partitions based on dissimilarity or randomness. This work offers a departuring step on the efficiency impact of vertical partitioning criteria along the different stages of pattern mining and biclustering algorithms. Availability: All the code is freely available at https://github.com/JupitersMight/pattern_merge under the MIT license.