The SPMF data mining library v.2.40 is released!

Hi all, I am please to announce that a new version of SPMF has just been published (v 2.40). It contains 9 novel algorithms:

  • the HUIM-ABC algorithm for mining high utility itemsets using Artificial Bee Colony Optimization (thanks to Wei Song and Chaoming Huang for contributing the code)
  • the TKG algorithm for mining the top-k frequent subgraphs in a graph database (thanks to Fournier-Viger, P. and Chao Cheng)
  • the gSpan algorithm for mining the frequent subgraphs in a graph database (thanks to Chao Cheng)
  • the SPP-Growth algorithm for mining stable periodic itemsets in a transaction database (by Peng Yang)
  • the MPFPS-BFS algorithm for mining periodic patterns common to multiple sequences (by Zhitian Li).
  • the MPFPS-DFS algorithm for mining periodic patterns common to multiple sequences (by Zhitian Li).
  • the NAFCP algorithm for mining frequent closed itemsets (thanks to Nader Aryabarzan et al.)
  • the OPUS-Miner algorithm for mining self-sufficient itemsets (thanks to Xiang Li for converting the original C++ code to Java)

It also includes some bug fixes and other minor improvements.

I did not release a new version of SPMF since a few months because I was quite busy recently. But the SPMF project is still very active. I am currently working on preparing a few more algorithms for release. I will try to make the next release in November.

Also I would like to say thanks again to all the persons who have contributed, used, cited, and supported the software! This is really helpful! Moreover, all contributions are always welcome.


Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 150 data mining algorithms.

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