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License: MIT License
Repository for all the code and resources associated to the paper Language-Model Based Informed Partition of Databases to Speed Up Pattern Mining
License: MIT License
Apply the KRIMP post-accepting pruning mechanism to the naive merging procedure.
Take into account that the support of the codes in the database does not change when adding / removing codes from the codetable, so only the usages must be updated (this is also to be taken into account in the issue #1 )
We need a method to include the information of the embeddings into the merging process.
Firstly, we could directly adapt the SLIM criteria to cos/euclidean distances between centroids of the codes and check whether they contribute or not (KRIMP pruning mechanism should be applied though).
After mining, apparently some items have no actual translation back to the .dat format, leading to support 0 codes (which should never happen).
The current version of the support and usage calculations is too slow in python, we must parallelize it. Check the possible methods in python and use one of them.
The main problem is that is it using the ct.calculate_size_database_from_codetable (only the size of the database), while at this point it should be using ct.calcualte_complete_size (https://github.com/MaillPierre/SWKrimpSim)
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