Hi, that does not really sound like a clustering but more like a preprocessing problem to me. For each item you want to calculate the length of the longest subsequence of "1"s. That could be done by a simple function and would create a new (one-dimensional) property for each of your items. You could then apply any clustering algorithm to this feature (i.e. you'd be clustering a one-dimensional dataset)...
Regards, Christian lampahome <pahome.c...@mirlab.org> schrieb am Mi., 3. Apr. 2019 um 11:08 Uhr: > 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 > > What I want to cluster is the length of continuous duration > Ex: > ID=3 > 2 > 1 = 0 > > Can any distance metric to help clustering based on the length of > continuous duration? > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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