Hi All, Just a short comment to "If you had an alternative algorithm for frequent itemset generation in mind (I am not sure if others exist, to be honest). I would also be happy about that one, too." There are many other techniques and their modifications for related problems like sequence mining, see e.g. here: http://www.philippe-fournier-viger.com/spmf/. In my opinion, a notable difference for practice exists between frequent itemsets and closed (frequent) itemsets; the latter may reduce an output drastically. However, combinatorial explosion w.r.t. the number of produced patterns is an issue here.
Best, Dmitry пн, 17 дек. 2018 г. в 10:12, Sebastian Raschka <m...@sebastianraschka.com>: > Hi Rui, > > I agree with Joel that association rule mining could be a bit tricky to > fit nicely within the scikit-learn API. Maybe this could be some > transformer class? I thought about that a few years ago but remember that I > couldn't come up with a good solution at that point. > > In any case, I have an association rule implementation in mlxtend ( > http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/), > which is based on the apriori algorithm. Some users were asking about Eclat > and FP-Growth algorithms, instead of apriori. If you are interested in such > a contribution, i.e., implementing Eclat or FP-Growth such that instead of > > frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True) > association_rules(frequent_itemsets, metric="confidence", > min_threshold=0.7) > > one could use > > frequent_itemsets = eclat(df, min_support=0.6, use_colnames=True) > > or > > frequent_itemsets = fpgrowth(df, min_support=0.6, use_colnames=True) > association_rules(frequent_itemsets, metric="confidence", > min_threshold=0.7) > > I would be very happy about such a contribution (see issue tracker at > https://github.com/rasbt/mlxtend/issues/248) > > If you had an alternative algorithm for frequent itemset generation in > mind (I am not sure if others exist, to be honest). I would also be happy > about that one, too. > > Best, > Sebastian > > > On Dec 17, 2018, at 12:26 AM, Joel Nothman <joel.noth...@gmail.com> > wrote: > > > > Hi Rui, > > > > This has been discussed several times on the mailing list and issue > tracker. We are not interested in association rule mining in Scikit-learn > for its own purposes. We would be interested in association rule mining > only as part of a classification algorithm. Are there such algorithms which > are mature and popular enough to meet our inclusion criteria (see our FAQ)? > > > > Cheers, > > > > Joel > > > > On Mon, 17 Dec 2018 at 09:24, rui min <minminm...@hotmail.com> wrote: > > Dear scikit-learn developers, > > > > I am Rui from Spain, Granada University. Currently I am planning to > write an association rule algorithm in scikit-learn. > > I don’t know if anyone is working on that. So avoid duplication of > the work, I would like to ask here. > > > > Hope to hear from you soon. > > > > > > Best Regards > > > > > > Rui > > > > > > > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn