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SPMF 2.41 is released! (2020-2-26)
Posted by:
Philippe
Date: February 25, 2020 11:00PM
Hi all,
Thanks again to all those who have been using the SPMF library in their research or applications. This is to let you know that a new version has been released (SPMF v2.41). It offers six new algorithms:
- the QCSP algorithm for mining the top-k quantive cohesive sequential patterns in a single sequence or in multiple sequences (thanks to Lens Fereman et al.)
- the MRCPPS algorithm for mining rare correlated periodic patterns common to multiple sequences (thanks to Peng Yang et al.)
- the HUE-SPAN algorithm for efficiently mining high-utility episodes in a sequence of events with utility information (thanks to Peng Yang et al.)
- the EMMA, MINEPI and MINEPI+ algorithms for mining frequent episodes in a sequence of events (thanks to Peng Yang et al.)
Besides, a few bugs have also been fixed.
Moreover, I have also improved the layout of the datasets page on the website.
I will also add four or five more algorithms soon, that I did not have time to integrate in this version.
Also, if you have some Java implementations of algorithms, welcome to send me your code if you wish to integrate it in SPMF. You will then become a contributor and your name will appear on the website!
Best regards,
Philippe
Edited 2 time(s). Last edit at 02/25/2020 11:04PM by webmasterphilfv.
Thanks again to all those who have been using the SPMF library in their research or applications. This is to let you know that a new version has been released (SPMF v2.41). It offers six new algorithms:
- the QCSP algorithm for mining the top-k quantive cohesive sequential patterns in a single sequence or in multiple sequences (thanks to Lens Fereman et al.)
- the MRCPPS algorithm for mining rare correlated periodic patterns common to multiple sequences (thanks to Peng Yang et al.)
- the HUE-SPAN algorithm for efficiently mining high-utility episodes in a sequence of events with utility information (thanks to Peng Yang et al.)
- the EMMA, MINEPI and MINEPI+ algorithms for mining frequent episodes in a sequence of events (thanks to Peng Yang et al.)
Besides, a few bugs have also been fixed.
Moreover, I have also improved the layout of the datasets page on the website.
I will also add four or five more algorithms soon, that I did not have time to integrate in this version.
Also, if you have some Java implementations of algorithms, welcome to send me your code if you wish to integrate it in SPMF. You will then become a contributor and your name will appear on the website!
Best regards,
Philippe
Edited 2 time(s). Last edit at 02/25/2020 11:04PM by webmasterphilfv.