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PAttern MIning (PAMI) is a Python library containing several algorithms to discover user interest-based patterns in a wide-spectrum of datasets across multiple computing platforms. Useful links to utilize the services of this library were provided below:
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Youtube tutorial https://www.youtube.com/playlist?list=PLKP768gjVJmDer6MajaLbwtfC9ULVuaCZ
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Tutorials (Notebooks) https://github.com/UdayLab/PAMI/tree/main/notebooks
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User manual https://udaylab.github.io/PAMI/manuals/index.html
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Coders manual https://udaylab.github.io/PAMI/codersManual/index.html
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Code documentation https://pami-1.readthedocs.io
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Discussions on PAMI usage https://github.com/UdayLab/PAMI/discussions
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Report issues https://github.com/UdayLab/PAMI/issues
- ✅ Well-tested and production-ready
- 🔋 Highly optimized to our best effort, light-weight, and energy-efficient
- 👀 Proper code documentation
- 🍼 Ample examples of using various algorithms at ./notebooks folder
- 🤖 Works with AI libraries such as TensorFlow, PyTorch, and sklearn.
- ⚡️ Supports Cuda and PySpark
- 🖥️ Operating System Independence
- 🔬 Knowledge discovery in static data and streams
- 🐎 Snappy
- 🐻 Ease of use
- Version 2023.07.07: New algorithms: cuApriroi, cuAprioriBit, cuEclat, cuEclatBit, gPPMiner, cuGPFMiner, FPStream, HUPMS, SHUPGrowth New codes to generate synthetic databases
- Version 2023.06.20: Fuzzy Partial Periodic, Periodic Patterns in High Utility, Code Documentation, help() function Update
- Version 2023.03.01: prefixSpan and SPADE
Total number of algorithms: 83
Installation
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Installing basic pami package (recommended)
pip install pami -
Installing pami package in a GPU machine that supports CUDA
pip install 'pami[gpu]' -
Installing pami package in a distributed network environment supporting Spark
pip install 'pami[spark]'
Upgradation
pip install --upgrade pami
Uninstallation
pip uninstall pami
Information
pip show pami
1.1. Frequent pattern mining: Sample
| Basic | Closed | Maximal | Top-k | CUDA | pyspark |
|---|---|---|---|---|---|
| Apriori |
CHARM |
maxFP-growth |
FAE |
cudaAprioriGCT | parallelApriori |
| FP-growth |
cudaAprioriTID | parallelFPGrowth |
|||
| ECLAT |
cudaEclatGCT | parallelECLAT |
|||
| ECLAT-bitSet |
|||||
| ECLAT-diffset |
1.2. Relative frequent pattern mining: Sample
| Basic |
|---|
| RSFP-growth |
1.3. Frequent pattern with multiple minimum support: Sample
| Basic |
|---|
| CFPGrowth |
| CFPGrowth++ |
1.4. Correlated pattern mining: Sample
| Basic |
|---|
| CoMine |
| CoMine++ |
| Basic |
|---|
| FTApriori |
| FTFPGrowth (under development) |
| Basic |
|---|
| CMine |
| CMine++ |
2.1. Periodic-frequent pattern mining: Sample
| Basic | Closed | Maximal | Top-K |
|---|---|---|---|
| PFP-growth |
CPFP |
maxPF-growth |
kPFPMiner |
| PFP-growth++ |
Topk-PFP |
||
| PS-growth |
|||
| PFP-ECLAT |
|||
| PFPM-Compliments |
2.2. Local periodic pattern mining: Sample
| Basic |
|---|
| LPPGrowth (under development) |
| LPPMBreadth (under development) |
| LPPMDepth (under development) |
2.3. Partial periodic-frequent pattern mining: Sample
| Basic |
|---|
| GPF-growth |
| PPF-DFS |
| GPPF-DFS |
2.4. Partial periodic pattern mining: Sample
| Basic | Closed | Maximal | topK | CUDA |
|---|---|---|---|---|
| 3P-growth |
3P-close |
max3P-growth |
topK-3P growth |
cuGPPMiner (under development) |
| 3P-ECLAT |
gPPMiner (under development) |
|||
| G3P-Growth |
2.5. Periodic correlated pattern mining: Sample
| Basic |
|---|
| EPCP-growth |
2.6. Stable periodic pattern mining: Sample
| Basic | TopK |
|---|---|
| SPP-growth |
TSPIN |
| SPP-ECLAT |
2.7. Recurring pattern mining: Sample
| Basic |
|---|
| RPgrowth |
3.1. Geo-referenced frequent pattern mining: Sample
| Basic |
|---|
| spatialECLAT |
| FSP-growth |
3.2. Geo-referenced periodic frequent pattern mining: Sample
| Basic |
|---|
| GPFPMiner |
| PFS-ECLAT |
| ST-ECLAT |
3.3. Geo-referenced partial periodic pattern mining:Sample
| Basic |
|---|
| STECLAT |
4.1. High utility pattern mining: Sample
| Basic |
|---|
| EFIM |
| HMiner |
| UPGrowth |
4.2. High utility frequent pattern mining: Sample
| Basic |
|---|
| HUFIM |
4.3. High utility geo-referenced frequent pattern mining: Sample
| Basic |
|---|
| SHUFIM |
4.4. High utility spatial pattern mining: Sample
| Basic | topk |
|---|---|
| HDSHIM |
TKSHUIM |
| SHUIM |
4.5. Relative High utility pattern mining: Sample
| Basic |
|---|
| RHUIM |
4.6. Weighted frequent pattern mining: Sample
| Basic |
|---|
| WFIM |
4.7. Weighted frequent regular pattern mining: Sample
| Basic |
|---|
| WFRIMiner |
4.8. Weighted frequent neighbourhood pattern mining: Sample
| Basic |
|---|
| SSWFPGrowth |
5.1. Fuzzy Frequent pattern mining: Sample
| Basic |
|---|
| FFI-Miner |
5.2. Fuzzy correlated pattern mining: Sample
| Basic |
|---|
| FCP-growth |
5.3. Fuzzy geo-referenced frequent pattern mining: Sample
| Basic |
|---|
| FFSP-Miner |
5.4. Fuzzy periodic frequent pattern mining: Sample
| Basic |
|---|
| FPFP-Miner |
5.5. Fuzzy geo-referenced periodic frequent pattern mining: Sample
| Basic |
|---|
| FGPFP-Miner (under development) |
6.1. Uncertain frequent pattern mining: Sample
| Basic | top-k |
|---|---|
| PUF |
TUFP |
| TubeP |
|
| TubeS |
|
| UVEclat |
6.2. Uncertain periodic frequent pattern mining: Sample
| Basic |
|---|
| UPFP-growth |
| UPFP-growth++ |
6.3. Uncertain Weighted frequent pattern mining: Sample
| Basic |
|---|
| WUFIM |
7.1. Sequence frequent pattern mining: Sample
| Basic |
|---|
| SPADE |
| PrefixSpan |
| Basic |
|---|
| GFSP-Miner (under development) |
| Basic |
|---|
| PP-Growth (under development) |
- Frequent pattern mining
| Basic |
|---|
| to be written |
- High utility pattern mining
| Basic |
|---|
| HUPMS |
| Basic |
|---|
| PositionMining |
coming soon