pami 2024.2.15.2

This software is being developed at the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

Click here for more information

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. Youtube tutorial https://www.youtube.com/playlist?list=PLKP768gjVJmDer6MajaLbwtfC9ULVuaCZ

2. Tutorials (Notebooks) https://github.com/UdayLab/PAMI/tree/main/notebooks

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

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

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

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

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

8. 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

1. Installing basic pami package (recommended)

   pip install pami
   

2. Installing pami package in a GPU machine that supports CUDA

   pip install 'pami[gpu]'
   

3. 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

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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				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++

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++		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. 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

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. 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
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. 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

1. Frequent pattern mining

Basic
to be written

2. 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

11. Mining pattrens from Graphs

coming soon