Widened KRIMP: Better Performance through Diverse Parallelism

@inproceedings{Sampson2014WidenedKB,
  title={Widened KRIMP: Better Performance through Diverse Parallelism},
  author={Oliver Sampson and Michael R. Berthold},
  booktitle={IDA},
  year={2014}
}
We demonstrate that the previously introduced Widening framework is applicable to state-of-the-art Machine Learning algorithms. Using Krimp, an itemset mining algorithm, we show that parallelizing the search finds better solutions in nearly the same time as the original, sequential/greedy algorithm. We also introduce Reverse Standard Candidate Order (RSCO) as a candidate ordering heuristic for Krimp.