Package: pami
Click here for more informationIntroductionPAttern 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:Youtube tutorialhttps://www.youtube.com/playlist?list=PLKP768gjVJmDer6MajaLbwtfC9ULVuaCZTutorials (Notebooks)https://github.com/UdayLab/PAMI/tree/main/notebooksUser manualhttps://udaylab.github.io/PAMI/manuals/index.htmlCoders manualhttps://udaylab.github.io/PAMI/codersManual/index.htmlCode documentationhttps://pami-1.readthedocs.ioDatasetshttps://u-aizu.ac.jp/~udayrage/datasets.htmlDiscussions on PAMI usagehttps://github.com/UdayLab/PAMI/discussionsReport issueshttps://github.com/UdayLab/PAMI/issuesFeatures✅ 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./notebooksfolder🤖 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 useRecent versionsVersion 2023.07.07: New algorithms: cuApriroi, cuAprioriBit, cuEclat, cuEclatBit, gPPMiner, cuGPFMiner, FPStream, HUPMS, SHUPGrowth New codes to generate synthetic databasesVersion 2023.06.20: Fuzzy Partial Periodic, Periodic Patterns in High Utility, Code Documentation, help() function UpdateVersion 2023.03.01: prefixSpan and SPADETotal number of algorithms: 83MaintenanceInstallationInstalling basic pami package (recommended)pip install pamiInstalling pami package in a GPU machine that supports CUDApip install 'pami[gpu]'Installing pami package in a distributed network environment supporting Sparkpip install 'pami[spark]'Upgradationpip install --upgrade pamiUninstallationpip uninstall pamiInformationpip show pamiTutorials1. Pattern mining in binary transactional databases1.1. Frequent pattern mining:SampleBasicClosedMaximalTop-kCUDApysparkAprioriCHARMmaxFP-growthFAEcudaAprioriGCTparallelAprioriFP-growthcudaAprioriTIDparallelFPGrowthECLATcudaEclatGCTparallelECLATECLAT-bitSetECLAT-diffset1.2. Relative frequent pattern mining:SampleBasicRSFP-growth1.3. Frequent pattern with multiple minimum support:SampleBasicCFPGrowthCFPGrowth++1.4. Correlated pattern mining:SampleBasicCoMineCoMine++1.5. Fault-tolerant frequent pattern mining (under development)BasicFTAprioriFTFPGrowth (under development)1.6. Coverage pattern mining (under development)BasicCMineCMine++2. Pattern mining in binary temporal databases2.1. Periodic-frequent pattern mining:SampleBasicClosedMaximalTop-KPFP-growthCPFPmaxPF-growthkPFPMinerPFP-growth++Topk-PFPPS-growthPFP-ECLATPFPM-Compliments2.2. Local periodic pattern mining:SampleBasicLPPGrowth  (under development)LPPMBreadth (under development)LPPMDepth   (under development)2.3. Partial periodic-frequent pattern mining:SampleBasicGPF-growthPPF-DFSGPPF-DFS2.4. Partial periodic pattern mining:SampleBasicClosedMaximaltopKCUDA3P-growth3P-closemax3P-growthtopK-3P growthcuGPPMiner (under development)3P-ECLATgPPMiner (under development)G3P-Growth2.5. Periodic correlated pattern mining:SampleBasicEPCP-growth2.6. Stable periodic pattern mining:SampleBasicTopKSPP-growthTSPINSPP-ECLAT2.7. Recurring pattern mining:SampleBasicRPgrowth3. Mining patterns from binary Geo-referenced (or spatiotemporal) databases3.1. Geo-referenced frequent pattern mining:SampleBasicspatialECLATFSP-growth3.2. Geo-referenced periodic frequent pattern mining:SampleBasicGPFPMinerPFS-ECLATST-ECLAT3.3. Geo-referenced partial periodic pattern mining:SampleBasicSTECLAT4. Mining patterns from Utility (or non-binary) databases4.1. High utility pattern mining:SampleBasicEFIMHMinerUPGrowth4.2. High utility frequent pattern mining:SampleBasicHUFIM4.3. High utility geo-referenced frequent pattern mining:SampleBasicSHUFIM4.4. High utility spatial pattern mining:SampleBasictopkHDSHIMTKSHUIMSHUIM4.5. Relative High utility pattern mining:SampleBasicRHUIM4.6. Weighted frequent pattern mining:SampleBasicWFIM4.7. Weighted frequent regular pattern mining:SampleBasicWFRIMiner4.8. Weighted frequent neighbourhood pattern mining:SampleBasicSSWFPGrowth5. Mining  patterns from fuzzy transactional/temporal/geo-referenced databases5.1. Fuzzy Frequent pattern mining:SampleBasicFFI-Miner5.2. Fuzzy correlated pattern mining:SampleBasicFCP-growth5.3. Fuzzy geo-referenced frequent pattern mining:SampleBasicFFSP-Miner5.4. Fuzzy periodic frequent pattern mining:SampleBasicFPFP-Miner5.5. Fuzzy geo-referenced periodic frequent pattern mining:SampleBasicFGPFP-Miner (under development)6. Mining patterns from uncertain transactional/temporal/geo-referenced databases6.1. Uncertain frequent pattern mining:SampleBasictop-kPUFTUFPTubePTubeSUVEclat6.2. Uncertain periodic frequent pattern mining:SampleBasicUPFP-growthUPFP-growth++6.3. Uncertain Weighted frequent pattern mining:SampleBasicWUFIM7. Mining patterns from sequence databases7.1. Sequence frequent pattern mining:SampleBasicSPADEPrefixSpan7.2. Geo-referenced Frequent Sequence Pattern miningBasicGFSP-Miner (under development)8. Mining patterns from multiple timeseries databases8.1. Partial periodic pattern mining (under development)BasicPP-Growth (under development)9. Mining interesting patterns from StreamsFrequent pattern miningBasicto be writtenHigh utility pattern miningBasicHUPMS10. Mining patterns from contiguous character sequences (E.g., DNA, Genome, and Game sequences)10.1. Contiguous Frequent PatternsBasicPositionMining11. Mining pattrens from Graphscoming soon