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• Introduction
• Development process
• Recent updates
• Features
• Maintenance
• Try your first PAMI program
• Evaluation
• Reading Material
• License
• Documentation
• Background
• Getting Help
• Discussion and Development
• Contribution to PAMI
• Tutorials
• Real-World Case Studies

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

• Version 2024.05.01: In this latest version, the following updates have been made:
  • Included two new algorithms, Gspan and TKG, for frequent subgraph mining.
  • Updated three Synthetic Data Generator, transactional database, temporal database, and geo-referenced transactional database.
  • Optimized the following frequent pattern mining algorithms: Apriori, Aprioribitset, ECLAT, ECLATbitset, FPGrowth, and CHARM.
  • startMine() function has been deprecated to mine() function.

Total number of algorithms: 83

• ✅ 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

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]'
   

4. Installing pami package for developing purpose

   pip install 'pami[dev]'
   

5. Installing complete Library of pami

   pip install 'pami[all]'
   

Upgradation

    pip install --upgrade pami

Uninstallation

    pip uninstall pami 

Information

    pip show pami

$ python

# first import pami 
from PAMI.frequentPattern.basic import FPGrowth as alg
fileURL = "https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv"
minSup=300
obj = alg.FPGrowth(iFile=fileURL, minSup=minSup, sep='\t')
obj.startMine()
obj.save('frequentPatternsAtMinSupCount300.txt')
frequentPatternsDF= obj.getPatternsAsDataFrame()
print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns
print('Runtime: ' + str(obj.getRuntime())) #measure the runtime
print('Memory (RSS): ' + str(obj.getMemoryRSS()))
print('Memory (USS): ' + str(obj.getMemoryUSS()))

Output:
Frequent patterns were generated successfully using frequentPatternGrowth algorithm
Total No of patterns: 4540
Runtime: 8.749667644500732
Memory (RSS): 522911744
Memory (USS): 475353088

1. we compared three different Python libraries such as PAMI, mlxtend and efficient-apriori for Apriori.
2. (Transactional_T10I4D100K.csv)is a transactional database downloaded from PAMI and used as an input file for all libraries.
3. Minimum support values and seperator are also same.

• The performance of the Apriori algorithm is shown in the graphical results below:

1. Comparing the Patterns Generated by different Python libraries for the Apriori algorithm:

   

2. Evaluating the Runtime of the Apriori algorithm across different Python libraries:

   

3. Comparing the Memory Consumption of the Apriori algorithm across different Python libraries:

   

For more information, we have uploaded the evaluation file in two formats:

• One ipynb file format, please check it here. Evaluation File ipynb
• Two pdf file format, check here. Evaluation File Pdf

For more examples, refer this YouTube link YouTube

The official documentation is hosted on PAMI.

The idea and motivation to develop PAMI was from Kitsuregawa Lab at the University of Tokyo. Work on PAMI started at University of Aizu in 2020 and has been under active development since then.

For any queries, the best place to go to is Github Issues GithubIssues.

In our GitHub repository, the primary platform for discussing development-related matters is the university lab. We encourage our team members and contributors to utilize this platform for a wide range of discussions, including bug reports, feature requests, design decisions, and implementation details.

We invite and encourage all community members to contribute, report bugs, fix bugs, enhance documentation, propose improvements, and share their creative ideas.

Basic
Confidence
Lift
Leverage

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

Basic
RSFP-growth

Basic
CFPGrowth
CFPGrowth++

Basic
CoMine
CoMine++

Basic
FTApriori
FTFPGrowth (under development)

Basic
CMine
CMine++

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

Basic
LPPGrowth (under development)
LPPMBreadth (under development)
LPPMDepth (under development)

Basic
GPF-growth
PPF-DFS
GPPF-DFS

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

Basic
EPCP-growth

Basic	TopK
SPP-growth	TSPIN
SPP-ECLAT

Basic
RPgrowth

Basic
spatialECLAT
FSP-growth

Basic
GPFPMiner
PFS-ECLAT
ST-ECLAT

Basic
STECLAT

Basic
EFIM
HMiner
UPGrowth

Basic
HUFIM

Basic
SHUFIM

Basic	topk
HDSHIM	TKSHUIM
SHUIM

Basic
RHUIM

Basic
WFIM

Basic
WFRIMiner

Basic
FFI-Miner

Basic
FCP-growth

Basic
FFSP-Miner

Basic
FPFP-Miner

Basic
FGPFP-Miner (under development)

Basic	top-k
PUF	TUFP
TubeP
TubeS
UVEclat

Basic
UPFP-growth
UPFP-growth++

Basic
WUFIM

Basic
SPADE
PrefixSpan

Basic
GFSP-Miner (under development)

Basic
PP-Growth (under development)

1. Frequent pattern mining

2. High utility pattern mining

Basic
PositionMining

Basic	topk
Gspan	TKG

Database type
Transactional database
Temporal database
Utility database (coming soon)
spatio-transactional database (coming soon)
spatio-temporal database (coming soon)
fuzzy transactional database (coming soon)
fuzzy temporal database (coming soon)
Sequence database generator (coming soon)

Approaches
Transactional database
Temporal database
Utility database (coming soon)

1. Air pollution analytics

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