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 approximate high utility itemset mining 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.
