Project description
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Introduction
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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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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Datasets https://u-aizu.ac.jp/~udayrage/datasets.html
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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
Features
- ✅ 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
Recent versions
- 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
Maintenance
Installation
pip install pami
pip install 'pami[gpu]'
pip install 'pami[spark]'
Updation
pip install --upgrade pami
Uninstallation
pip uninstall pami
Tutorials
1. Pattern mining in binary transactional databases
1.1. Frequent pattern mining: Sample
Basic |
Closed |
Maximal |
Top-k |
CUDA |
pyspark |
Apriori |
CHARM |
maxFP-growth |
FAE |
cudaAprioriGCT |
parallelApriori |
FP-growth |
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cudaAprioriTID |
parallelFPGrowth |
ECLAT |
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cudaEclatGCT |
parallelECLAT |
ECLAT-bitSet |
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ECLAT-diffset |
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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++ |
1.5. Fault-tolerant frequent pattern mining (under development)
Basic |
FTApriori |
FTFPGrowth (under development) |
1.6. Coverage pattern mining (under development)
Basic |
CMine |
CMine++ |
2. Pattern mining in binary temporal databases
2.1. Periodic-frequent pattern mining: Sample
Basic |
Closed |
Maximal |
Top-K |
PFP-growth |
CPFP |
maxPF-growth |
kPFPMiner |
PFP-growth++ |
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Topk-PFP |
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PS-growth |
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PFP-ECLAT |
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PFPM-Compliments |
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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 |
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gPPMiner (under development) |
G3P-Growth |
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2.5. Periodic correlated pattern mining: Sample
Basic |
EPCP-growth |
2.6. Stable periodic pattern mining: Sample
Basic |
TopK |
SPP-growth |
TSPIN |
SPP-ECLAT |
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2.7. Recurring pattern mining: Sample
Basic |
RPgrowth |
3. Mining patterns from binary Geo-referenced (or spatiotemporal) databases
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. Mining patterns from Utility (or non-binary) databases
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 |
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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
5. Mining patterns from fuzzy transactional/temporal/geo-referenced databases
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. Mining patterns from uncertain transactional/temporal/geo-referenced databases
6.1. Uncertain frequent pattern mining: Sample
Basic |
top-k |
PUF |
TUFP |
TubeP |
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TubeS |
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UVEclat |
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6.2. Uncertain periodic frequent pattern mining: Sample
Basic |
UPFP-growth |
UPFP-growth++ |
6.3. Uncertain Weighted frequent pattern mining: Sample
Basic |
WUFIM |
7. Mining patterns from sequence databases
7.1. Sequence frequent pattern mining: Sample
Basic |
SPADE |
PrefixSpan |
7.2. Geo-referenced Frequent Sequence Pattern mining
Basic |
GFSP-Miner (under development) |
8. Mining patterns from multiple timeseries databases
8.1. Partial periodic pattern mining (under development)
Basic |
PP-Growth (under development) |
9. Mining interesting patterns from Streams
- Frequent pattern mining
- High utility pattern mining
10. Mining patterns from contiguous character sequences (E.g., DNA, Genome, and Game sequences)
10.1. Contiguous Frequent Patterns
Basic |
PositionMining |
11. Mining pattrens from Graphs
coming soon
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