When clustering it's often a good idea to think not about the algorithm used to identify clusters, but about what distance metric might capture your intuitions about similar and dissimilar points. HTH
On Fri., 29 Mar. 2019, 6:39 pm lampahome, <pahome.c...@mirlab.org> wrote: > I have data which contain access duration of each items. > > EX: t0~t4 is the access time duration. 1 means the item was accessed in > the time duration, 0 means not. > ID,t0,t1,t2,t3,t4 > 0,1,0,0,1 > 1,1,0,0,1 > 2,0,0,1,1 > 3,0,1,1,1 > > Can cluster the group which item will access for a continuous duration? > > Like above, ID=2,ID=3 are what I want. > > I try KMeans, DBSCAN but it seems doesn't well > > Is there any algo recommended? > > thx > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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