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PAMI_PyKit

PAMI_PyKit stands for PAttern MIning Python Kit. It contains a set of python libraries to discover user interest-based patterns in very large databases. The python programs in this kit are organized in the following topics:

  1. Traditional algorithms - Conventional algorithms whose input exists in the form of transactional databases (or files).

  2. Parallel algorithms - Parallel pattern mining algorithms based on Map-Reduce framework.

Traditional (or sequential) algorithms

  1. frequentPatternGrowth (FPgrowth)
  2. multipleSupportFrequentPatternGrowth using user specified minimum item supports (MSFPgrowth)
  3. multipleSupportFrequentPatternGrowth using percentage based function (IMSFPgrowth)
  4. correlatedPatternGrowth (CPgrowth)
  5. periodicFrequentPattern-growth (PFPgrowth)
  6. periodicFrequentPattern-growth with periodic summaries (PSgrowth)
  7. ITL-growth
  8. periodicFrequentPattern-growth using greedy search (PFPgrowthGS)
  9. periodicFrequentPattern-growth using multiple minimum supports and maximum periodicities (MSPFP-growth)
  10. periodicFrequentPattern-growth using periodic-ratio (PFPgrowthPR)
  11. partialPeriodicFrequentPattern-growth using period-support (PPFPgrowth)
  12. partialPeriodicFrequentPattern-growth using multiple period-supports (PPFPgrowth_MPS)
  13. recurringPattern-growth (RP-growth)
  14. HighUtilityItemsetMining (EFIM)
  15. HighUtilityFrequentItemsetMining (EFIMpp)
  16. SpatialHighUtilityItemsetMining
  17. PartialPeriodicSpatialPatternMining ()

Parallel algorithms

  1. parallelFrequentPatternGrowth
  2. parallel PeriodicFrequentPatternGrowth