PAMI stands for PAttern MIning. It constitutes several pattern mining algorithms to discover interesting patterns in transactional/temporal/spatiotemporal databases

Related tags

Deep LearningPAMI
Overview

PyPI AppVeyor PyPI - Python Version GitHub all releases GitHub license PyPI - Implementation PyPI - Wheel PyPI - Status GitHub issues GitHub forks GitHub stars

Introduction

PAMI stands for PAttern MIning. It constitutes several pattern mining algorithms to discover interesting patterns in transactional/temporal/spatiotemporal databases. This software is provided under GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007.

  1. The user manual for PAMI library is available at https://udayrage.github.io/PAMI/index.html
  2. Datasets to implement PAMI algorithms are available at https://www.u-aizu.ac.jp/~udayrage/software.html
  3. Please report issues in the software at https://github.com/udayRage/PAMI/issues

Installation

   pip install pami

Upgrade

   pip install --upgrade pami

Details

Total available algorithms: 43

  1. Frequent pattern mining:

    Basic Closed Maximal Top-k
    Apriori Closed maxFP-growth topK
    FP-growth
    ECLAT
    ECLAT-bitSet

  2. Frequent pattern mining using other measures:

    Basic
    RSFP

  3. Correlated pattern mining:

    Basic
    CP-growth
    CP-growth++

  4. Frequent spatial pattern mining:

    Basic
    spatialECLAT
    FSP-growth ?

  5. Correlated spatial pattern mining:

    Basic
    SCP-growth

  6. Fuzzy correlated pattern mining:

    Basic
    CFFI

  7. Fuzzy frequent spatial pattern mining:

    Basic
    FFSI

  8. Fuzzy periodic frequent pattern mining:

    Basic
    FPFP-Miner

  9. High utility frequent spatial pattern mining:

    Basic
    HDSHUIM

  10. High utility pattern mining:

    Basic
    EFIM
    UPGrowth

  11. Partial periodic frequent pattern:

    Basic
    GPF-growth
    PPF-DFS

  12. Periodic frequent pattern mining:

    Basic Closed Maximal
    PFP-growth CPFP maxPF-growth
    PFP-growth++
    PS-growth
    PFP-ECLAT

  13. Partial periodic pattern mining:

    Basic Maximal
    3P-growth max3P-growth
    3PECLAT

  14. Uncertain correlated pattern mining:

    Basic
    CFFI

  15. Uncertain frequent pattern mining:

    Basic
    PUF
    TubeP
    TubeS

  16. Uncertain periodic frequent pattern mining:

    Basic
    PTubeP
    PTubeS
    UPFP-growth

  17. Local periodic pattern mining:

    Basic
    LPPMbredth
    LPPMdepth
    LPPGrowth

  18. Recurring pattern mining:

    Basic
    RPgrowth
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Comments
  • Questions on how to use it

    Questions on how to use it

    Hello, I am a researcher that recently encountered a problem which requires me to use sequence pattern mining algorithm, so I found this package which is perfect. However, I still have some issues using it because there is too little information and documentation on this project, I don't know how to do the visualization and how to switch algorithms. It would be great if there is more manual, tutorial, etc.

    opened by Wandaboma 3
  • Error on converting a sparse dataframe into a transactional database

    Error on converting a sparse dataframe into a transactional database

    When trying to convert a sparse dataframe into a transactional database, through the code provided on link the following error appears : " AttributeError: module 'PAMI.extras.DF2DB.sparseDF2DB' has no attribute 'sparse2DB'. "

    Firstly, I simply change the word sparse2DB to sparseDF2DB, but then a different error appears " ValueError: DataFrame constructor not properly called! " My dataframe was already imported into the Jupyter notebook when I called it to the function, however, I also tried to save it and export it as an excel file and import it directly on the function, however, nothing worked and the error persisted.

    Can you please help?

    Thanks in advance.

    opened by catarinarurbano 2
  • Categorical values and data requirements for algorithms

    Categorical values and data requirements for algorithms

    Thanks for developing this great library! can we use categorical data for the temporal database scenario? looking at the example databases, can we use only numeric data variables for all the algorithms?

    opened by nsankar 1
Releases(0.9.5.1)
Owner
RAGE UDAY KIRAN
Associate Professor at the University of Aizu, Japan.
RAGE UDAY KIRAN
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