Hi All,
i have four datasets as follows
data one
col1 cls_id
1 a
2 c
3 a
data two
col1 cls_id
1 d
6 e
8 d
data three
col1 cls_id
7 h
9 f
8 h
data four
col1 cls_id
10 m
5 l
9 m
Now from these tables i need to create a final table so that each common element from these tables have a common cls_id
and all associated elements to that common element in different table also do have same common cls_id.
* FYI Actually i was running optnet to link elements but because of data size i am not able to run in a single go.
Hence,I had to split the data and run optnet and try to merge these four tables .
In table one and two element 1 is common and 8 is a associated element further 8 is also present in table 3 with
7 attached to it.
hence 1,3,7,8 should share same cluster id
similarly 9 and 10 should have same id
Any help would be greatly appreciated.
Regards
So, what should the "final table" be for these data?
the final table should be something like this
col1 final_cls_id
1 h
2 c
3 h
5 l
6 e
7 h
8 h
9 m
10 m
so since value 1 in col1 indifferent tables i associated to 3 & 8 and 8 is again attached to 7 as they have same cls_id in different tables.
Hence I have assigned same cls_id to all these values.
I have a table which contains 70 million records but I am able to run proc optnet on some 15 million records only . Is there any way to run optnet based on group by condition.
Thanks and Regards
Are you using the concomp algorithm? What do you use as node labels (IDs)?
What do you use as node labels (IDs). Numbers or character strings?
Please post your optnet code.
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