I have data which contains access duration of some items.
t0~t5 is the access time duration, 1 means the items was accessed in the time duration, 0 means it wasn't.
ID,t0,t1,t2,t3,t4 0,0,0,1,1,1 1,0,1,1,1,1 2,0,1,1,0,0 3,1,1,0,0,1 4,1,1,0,0,1
Objective: Find sets which are all
1 for a continuous duration as longer as possible.
I can set the minimum continuous length
len==2, what I want is groups
ID=3,4 aren't because their
2 but ID=0,1` are more continuous than them.
DBSCAN, they all cluster
ID=3,4 as one group and it makes sense. But it doesn't do what I want.
Although I can predict which ID will be access, I still find the longer continuous time-series.
I don't have label it to identify which ID will be what I want.
I try above algorithm but didn't figure out, I thought possible way is pre-processing before clustering and find out after clustering.
Is there any possible pre-processing of data to reach what I want ? Or any ways to help me?