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License: GNU General Public License v3.0

Shell 0.01% Python 8.80% Jupyter Notebook 66.58% C++ 0.03% Makefile 0.01% HTML 24.44% JavaScript 0.09% CSS 0.05% Batchfile 0.01%

pami's Introduction

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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:

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

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

  3. Code documentation https://pami-1.readthedocs.io

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

  5. Discussions on PAMI usage https://github.com/UdayLab/PAMI/discussions

  6. 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 Open In Colab CHARM Open In Colab maxFP-growth Open In Colab FAE Open In Colab cudaAprioriGCT parallelApriori Open In Colab
FP-growth Open In Colab cudaAprioriTID parallelFPGrowth Open In Colab
ECLAT Open In Colab cudaEclatGCT parallelECLAT Open In Colab
ECLAT-bitSet Open In Colab
ECLAT-diffset Open In Colab

1.2. Relative frequent pattern mining: Sample

Basic
RSFP-growth Open In Colab

1.3. Frequent pattern with multiple minimum support: Sample

Basic
CFPGrowth Open In Colab
CFPGrowth++ Open In Colab

1.4. Correlated pattern mining: Sample

Basic
CoMine Open In Colab
CoMine++ Open In Colab

1.5. Fault-tolerant frequent pattern mining (under development)

Basic
FTApriori Open In Colab
FTFPGrowth (under development) Open In Colab

1.6. Coverage pattern mining (under development)

Basic
CMine Open In Colab
CMine++ Open In Colab

2. Pattern mining in binary temporal databases

2.1. Periodic-frequent pattern mining: Sample

Basic Closed Maximal Top-K
PFP-growth Open In Colab CPFP Open In Colab maxPF-growth Open In Colab kPFPMiner Open In Colab
PFP-growth++ Open In Colab Topk-PFP Open In Colab
PS-growth Open In Colab
PFP-ECLAT Open In Colab
PFPM-Compliments Open In Colab

2.2. Local periodic pattern mining: Sample

Basic
LPPGrowth (under development) Open In Colab
LPPMBreadth (under development) Open In Colab
LPPMDepth (under development) Open In Colab

2.3. Partial periodic-frequent pattern mining: Sample

Basic
GPF-growth Open In Colab
PPF-DFS Open In Colab
GPPF-DFS Open In Colab

2.4. Partial periodic pattern mining: Sample

Basic Closed Maximal topK CUDA
3P-growth Open In Colab 3P-close Open In Colab max3P-growth Open In Colab topK-3P growth Open In Colab cuGPPMiner (under development) Open In Colab
3P-ECLAT Open In Colab gPPMiner (under development) Open In Colab
G3P-Growth Open In Colab

2.5. Periodic correlated pattern mining: Sample

Basic
EPCP-growth Open In Colab

2.6. Stable periodic pattern mining: Sample

Basic TopK
SPP-growth Open In Colab TSPIN Open In Colab
SPP-ECLAT Open In Colab

2.7. Recurring pattern mining: Sample

Basic
RPgrowth Open In Colab

3. Mining patterns from binary Geo-referenced (or spatiotemporal) databases

3.1. Geo-referenced frequent pattern mining: Sample

Basic
spatialECLAT Open In Colab
FSP-growth Open In Colab

3.2. Geo-referenced periodic frequent pattern mining: Sample

Basic
GPFPMiner Open In Colab
PFS-ECLAT Open In Colab
ST-ECLAT Open In Colab

3.3. Geo-referenced partial periodic pattern mining:Sample

Basic
STECLAT Open In Colab

4. Mining patterns from Utility (or non-binary) databases

4.1. High utility pattern mining: Sample

Basic
EFIM Open In Colab
HMiner Open In Colab
UPGrowth Open In Colab

4.2. High utility frequent pattern mining: Sample

Basic
HUFIM Open In Colab

4.3. High utility geo-referenced frequent pattern mining: Sample

Basic
SHUFIM Open In Colab

4.4. High utility spatial pattern mining: Sample

Basic topk
HDSHIM Open In Colab TKSHUIM Open In Colab
SHUIM Open In Colab

4.5. Relative High utility pattern mining: Sample

Basic
RHUIM Open In Colab

4.6. Weighted frequent pattern mining: Sample

Basic
WFIM Open In Colab

4.7. Weighted frequent regular pattern mining: Sample

Basic
WFRIMiner Open In Colab

4.8. Weighted frequent neighbourhood pattern mining: Sample

Basic
SSWFPGrowth

5. Mining patterns from fuzzy transactional/temporal/geo-referenced databases

5.1. Fuzzy Frequent pattern mining: Sample

Basic
FFI-Miner Open In Colab

5.2. Fuzzy correlated pattern mining: Sample

Basic
FCP-growth Open In Colab

5.3. Fuzzy geo-referenced frequent pattern mining: Sample

Basic
FFSP-Miner Open In Colab

5.4. Fuzzy periodic frequent pattern mining: Sample

Basic
FPFP-Miner Open In Colab

5.5. Fuzzy geo-referenced periodic frequent pattern mining: Sample

Basic
FGPFP-Miner (under development) Open In Colab

6. Mining patterns from uncertain transactional/temporal/geo-referenced databases

6.1. Uncertain frequent pattern mining: Sample

Basic top-k
PUF Open In Colab TUFP
TubeP Open In Colab
TubeS Open In Colab
UVEclat

6.2. Uncertain periodic frequent pattern mining: Sample

Basic
UPFP-growth Open In Colab
UPFP-growth++ Open In Colab

6.3. Uncertain Weighted frequent pattern mining: Sample

Basic
WUFIM Open In Colab

7. Mining patterns from sequence databases

7.1. Sequence frequent pattern mining: Sample

Basic
SPADE Open In Colab
PrefixSpan Open In Colab

7.2. Geo-referenced Frequent Sequence Pattern mining

Basic
GFSP-Miner (under development) Open In Colab

8. Mining patterns from multiple timeseries databases

8.1. Partial periodic pattern mining (under development)

Basic
PP-Growth (under development) Open In Colab

9. Mining interesting patterns from Streams

  1. Frequent pattern mining
Basic
to be written
  1. High utility pattern mining
Basic
HUPMS

10. Mining patterns from contiguous character sequences (E.g., DNA, Genome, and Game sequences)

10.1. Contiguous Frequent Patterns

Basic
PositionMining Open In Colab

11. Mining pattrens from Graphs

coming soon

pami's People

Contributors

likhitha-palla avatar udayrage avatar avvari1830s avatar vanithakattumuri avatar pallamadhavi avatar saichitrab avatar nakamura204 avatar suzuki-zudai avatar pradeepppc avatar tarun-sreepada avatar kundai-kwangwari avatar sandeep0509 avatar raashika214 avatar saideepchennupati avatar raviua138 avatar shiridikumar avatar

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