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- Introduction
- Development process
- Inputs and outputs of a PAMI algorithm
- Recent updates
- Features
- Maintenance
- Try your first PAMI program
- Evaluation
- Reading Material
- License
- Documentation
- Background
- Getting Help
- Discussion and Development
- Contribution to PAMI
- Tutorials
- Association rule mining
- Mining transactional databases
- Mining temporal databases
- Mining spatiotemporal databases
- Mining utility databases
- Mining fuzzy databases
- Mining uncertain databases
- Mining sequence databases
- Mining multiple timeseries
- Mining streams
- Mining character sequences
- Mining graphs
- Additional features
- 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: NAME:SANGEETH
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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
- Version 2024.07.02:
In this latest version, the following updates have been made:
- Included one new algorithms, PrefixSpan, for Sequential Pattern.
- Optimized the following pattern mining algorithms: PFPGrowth, PFECLAT, GPFgrowth and PPF_DFS.
- Test cases are implemented for the following algorithms, Contiguous Frequent patterns, Correlated Frequent Patterns, Coverage Frequent Patterns, Fuzzy Correlated Frequent Patterns, Fuzzy Frequent Patterns, Fuzzy Georeferenced Patterns, Georeferenced Frequent Patterns, Periodic Frequent Patterns, Partial Periodic Frequent Patterns, HighUtility Frequent Patterns, HighUtility Patterns, HighUtility Georeferenced Frequent Patterns, Frequent Patterns, Multiple Minimum Frequent Patterns, Periodic Frequent Patterns, Recurring Patterns, Sequential Patterns, Uncertain Frequent Patterns, Weighted Uncertain Frequent Patterns.
- The algorithms mentioned below are automatically tested, Frequent Patterns, Correlated Frequent Patterns, Contiguous Frequent patterns, Coverage Frequent Patterns, Recurring Patterns, Sequential Patterns.
Total number of algorithms: 89
- ✅ Tested to the best of our possibility
- 🔋 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
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Installing basic pami package (recommended)
pip install pami
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Installing pami package in a GPU machine that supports CUDA
pip install 'pami[gpu]'
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Installing pami package in a distributed network environment supporting Spark
pip install 'pami[spark]'
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Installing pami package for developing purpose
pip install 'pami[dev]'
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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() #deprecated
obj.mine()
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
- we compared three different Python libraries such as PAMI, mlxtend and efficient-apriori for Apriori.
- (Transactional_T10I4D100K.csv)is a transactional database downloaded from PAMI and used as an input file for all libraries.
- Minimum support values and seperator are also same.
- The performance of the Apriori algorithm is shown in the graphical results below:
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Comparing the Patterns Generated by different Python libraries for the Apriori algorithm:
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Evaluating the Runtime of the Apriori algorithm across different Python libraries:
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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 |
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Confidence |
Lift |
Leverage |
1.1. Frequent pattern mining: Sample
1.2. Relative frequent pattern mining: Sample
Basic |
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RSFP-growth |
1.3. Frequent pattern with multiple minimum support: Sample
Basic |
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CFPGrowth |
CFPGrowth++ |
1.4. Correlated pattern mining: Sample
Basic |
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CoMine |
CoMine++ |
Basic |
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FTApriori |
FTFPGrowth (under development) |
Basic |
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CMine |
CMine++ |
2.1. Periodic-frequent pattern mining: Sample
Basic | Closed | Maximal | Top-K |
---|---|---|---|
PFP-growth |
CPFP |
maxPF-growth |
kPFPMiner |
PFP-growth++ |
Topk-PFP |
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PS-growth |
|||
PFP-ECLAT |
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PFPM-Compliments |
2.2. Local periodic pattern mining: Sample
Basic |
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LPPGrowth (under development) |
LPPMBreadth (under development) |
LPPMDepth (under development) |
2.3. Partial periodic-frequent pattern mining: Sample
Basic |
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GPF-growth |
PPF-DFS |
GPPF-DFS |
2.4. Partial periodic pattern mining: Sample
Basic | Closed | Maximal | topK | CUDA |
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3P-growth |
3P-close |
max3P-growth |
topK-3P growth |
cuGPPMiner (under development) |
3P-ECLAT |
gPPMiner (under development) |
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G3P-Growth |
2.5. Periodic correlated pattern mining: Sample
Basic |
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EPCP-growth |
2.6. Stable periodic pattern mining: Sample
Basic | TopK |
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SPP-growth |
TSPIN |
SPP-ECLAT |
2.7. Recurring pattern mining: Sample
Basic |
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RPgrowth |
3.1. Geo-referenced frequent pattern mining: Sample
Basic |
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spatialECLAT |
FSP-growth |
3.2. Geo-referenced periodic frequent pattern mining: Sample
Basic |
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GPFPMiner |
PFS-ECLAT |
ST-ECLAT |
3.3. Geo-referenced partial periodic pattern mining:Sample
Basic |
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STECLAT |
4.1. High utility pattern mining: Sample
Basic |
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EFIM |
HMiner |
UPGrowth |
4.2. High utility frequent pattern mining: Sample
Basic |
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HUFIM |
4.3. High utility geo-referenced frequent pattern mining: Sample
Basic |
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SHUFIM |
4.4. High utility spatial pattern mining: Sample
Basic | topk |
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HDSHIM |
TKSHUIM |
SHUIM |
4.5. Relative High utility pattern mining: Sample
Basic |
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RHUIM |
4.6. Weighted frequent pattern mining: Sample
Basic |
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WFIM |
4.7. Weighted frequent regular pattern mining: Sample
Basic |
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WFRIMiner |
4.8. Weighted frequent neighbourhood pattern mining: Sample
Basic |
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SSWFPGrowth |
5.1. Fuzzy Frequent pattern mining: Sample
Basic |
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FFI-Miner |
5.2. Fuzzy correlated pattern mining: Sample
Basic |
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FCP-growth |
5.3. Fuzzy geo-referenced frequent pattern mining: Sample
Basic |
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FFSP-Miner |
5.4. Fuzzy periodic frequent pattern mining: Sample
Basic |
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FPFP-Miner |
5.5. Fuzzy geo-referenced periodic frequent pattern mining: Sample
Basic |
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FGPFP-Miner (under development) |
6.1. Uncertain frequent pattern mining: Sample
Basic | top-k |
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PUF |
TUFP |
TubeP |
|
TubeS |
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UVEclat |
6.2. Uncertain periodic frequent pattern mining: Sample
Basic |
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UPFP-growth |
UPFP-growth++ |
6.3. Uncertain Weighted frequent pattern mining: Sample
Basic |
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WUFIM |
7.1. Sequence frequent pattern mining: Sample
Basic |
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SPADE |
PrefixSpan |
Basic |
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GFSP-Miner (under development) |
Basic |
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PP-Growth (under development) |
- Frequent pattern mining
Basic |
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to be written |
- High utility pattern mining
Basic |
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HUPMS |
Basic |
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PositionMining |
Basic | topk |
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Gspan |
TKG |
Basic |
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GTCP |
Approaches |
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Dense dataframe to databases |
Sparse dataframe to databases (coming soon) |
Approaches |
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Transactional database |
Temporal database |
Utility database (coming soon) |
Approaches |
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Subgraphs2FlatTransactions |
CSV2Parquet |
CSV2BitInteger |
CSV2Integer |
Approaches |
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Latex code (coming soon) |