Skip to content
forked from UdayLab/PAMI

PAMI is a Python library containing 80+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)

License

Notifications You must be signed in to change notification settings

vipulchhabra99/PAMI

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PyPI AppVeyor PyPI - Python Version GitHub all releases GitHub license PyPI - Implementation PyPI - Wheel PyPI - Status GitHub issues GitHub forks GitHub stars

Introduction

PAttern MIning (PAMI) is a Python library containing several algorithms to discover user interest-based patterns in transactional/temporal/geo-referencial/sequence databases across multiple computing platforms.

  1. User manual https://udayrage.github.io/PAMI/manuals/index.html

  2. Coders manual https://udayrage.github.io/PAMI/codersManual/index.html

  3. Datasets https://u-aizu.ac.jp/~udayrage/datasets.html

  4. Discussions on PAMI usage https://github.com/udayRage/PAMI/discussions

  5. Report issues https://github.com/udayRage/PAMI/issues

Recent versions

  • Version 2023.03.01: prefixSpan and SPADE

Total number of algorithms: 83

Maintenance

Installation

   pip install pami    

Updation

   pip install --upgrade pami

Uninstallation

   pip uninstall pami 

Tutorials

  • Click on "Basic" link to view the basic tutorial on using the algorithm.
  • Click on "Adv" link to view the advanced tutorial on using a particular algorithm.
  1. Frequent pattern mining: Sample
Basic Closed Maximal Top-k CUDA pyspark
Apriori Basic-Adv Closed Basic-Adv maxFP-growth Basic topK Basic-Adv cudaAprioriGCT parallelApriori Basic-Adv
FP-growth Basic-Adv cudaAprioriTID parallelFPGrowth Basic-Adv
ECLAT Basic-Adv cudaEclatGCT parallelECLAT Basic-Adv
ECLAT-bitSet Basic-Adv
ECLAT-diffset Basic-Adv
  1. Relative Frequent Patterns: Sample
Basic
RSFP Basic-Adv
  1. Frequent pattern with multiple minimum support: Sample
Basic
CFPGrowth
CFPGrowth++
  1. Correlated pattern mining: Sample
Basic
CP-growth Basic -Adv
CP-growth++ Basic -Adv
  1. Frequent spatial pattern mining: Sample
Basic
spatialECLAT Basic-Adv
FSP-growth Basic-Adv
  1. Fuzzy Frequent pattern mining: Sample
Basic
FFI-Miner Basic-Adv
  1. Fuzzy correlated pattern mining: Sample
Basic
FCP-growth Basic-Adv
  1. Fuzzy frequent spatial pattern mining: Sample
Basic
FFSP-Miner Basic-Adv
  1. Fuzzy periodic frequent pattern mining: Sample
Basic
FPFP-Miner Basic-Adv
  1. Geo referenced Fuzzy periodic frequent pattern mining: Sample
Basic
FPFP-Miner Basic-Adv
  1. High utility pattern mining: Sample
Basic
EFIM Basic-Adv
HMiner Basic-Adv
UPGrowth
  1. High utility frequent pattern mining: Sample
Basic
HUFIM Basic-Adv
  1. High utility frequent spatial pattern mining: Sample
Basic
SHUFIM Basic-Adv
  1. High utility spatial pattern mining: Sample
Basic topk
HDSHIM Basic-Adv TKSHUIM
SHUIM Basic
  1. Periodic frequent pattern mining: Sample
Basic Closed Maximal Top-K
PFP-growth Basic-Adv CPFP Basic-Adv maxPF-growth Basic-Adv kPFPMiner Basic-Adv
PFP-growth++ Basic-Adv
PS-growth Basic-Adv
PFP-ECLAT Basic-Adv
  1. Geo referenced Periodic frequent pattern mining: Sample
Basic
GPFPMiner Basic-Adv
  1. Local periodic pattern mining: Sample
Basic
LPPGrowth Basic
LPPMBreadth Basic
LPPMDepth Basic
  1. Partial periodic frequent pattern mining: Sample
Basic
GPF-growth Basic-Adv
PPF-DFS Basic-Adv
  1. Partial periodic pattern mining: Sample
Basic Closed Maximal
3P-growth Basic-Adv 3P-close Basic-Adv max3P-growth Basic
3P-ECLAT Basic-Adv
G3P-Growth Basic-Adv
  1. Partial periodic spatial pattern mining:Sample
Basic
STECLAT Basic-Adv
  1. Periodic correlated pattern mining: Sample
Basic
EPCP-growth Basic-Adv
  1. Stable periodic pattern mining: Sample
Basic TopK
SPP-growth Basic-Adv TSPIN
SPP-ECLAT Basic-Adv
  1. Uncertain frequent pattern mining: Sample
Basic top-k
PUF Basic-Adv TUFP
TubeP Basic-Adv
TubeS Basic-Adv
UVEclat
  1. Uncertain periodic frequent pattern mining: Sample
Basic
UPFP-growth Basic-Adv
UPFP-growth++ Basic-Adv
  1. Recurring pattern mining: Sample
Basic
RPgrowth Basic-Adv
  1. Relative High utility pattern mining: Sample
Basic
RHUIM Basic-Adv
  1. Weighted frequent pattern mining: Sample
Basic
WFIM Basic-Adv
  1. Uncertain Weighted frequent pattern mining: Sample
Basic
WUFIM Basic
  1. Weighted frequent regular pattern mining: Sample
Basic
WFRIMiner Basic-Adv
  1. Weighted frequent neighbourhood pattern mining: Sample
Basic
SSWFPGrowth
  1. Sequence frequent pattern mining: Sample
Basic
SPADE Basic-Adv
prefixSpan Basic-Adv

About

PAMI is a Python library containing 80+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • HTML 81.6%
  • Python 18.1%
  • JavaScript 0.2%
  • CSS 0.1%
  • TeX 0.0%
  • Batchfile 0.0%