I have a dataset like this:
jobname date avg start end A 30/03/2019 84900 03:13:25 20:59:47 B 02/04/2019 60798 16:53:05 16:00:05 C 02/04/2019 60798 16:53:05 16:00:04 D 02/04/2019 79200 22:00:01 15:00:39
The length is 100k, and I need to do a loop including a dataframe for each jobname, for example:
df1 = A, 30/03/2019, 84900, 03:13:25,20:59:47 df2 = B, 02/04/2019,60798,16:53:05,16:00:05
So I can use the kmeans algorithm to cluster all of the data.
How can I do this?
Hi @nsantiago17 ,
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Thanks for posting!
What's the end use case you're trying to achieve here? I have a similar sort of search that I use in a dashboard of my own. The data you have looks fairly simple in structure and so you could index the data, ensuring the fields are correctly extracted and then use a search something like the following:
index=<your_index> | kmeans k=<how_many_clusters> dt=<your_distype> <numeric_field_1> <numeric_field_2> cfield="Cluster number" | stats mode(<field1>) mode(<field2>) by "Cluster number" | rename mode(<field1>) AS "<Better Name>" mode(<field2>) AS "<Better Name>"
The search above is one I use to cluster patterns in airline traffic, grouping them by heading and altitude.
Hopefully that's enough to get going with - let me know if you have any more questions.
I need to cluster my dataset in 4 groups: those who have outliers early in the month, 15th, 21th and last day of the month by the jobname, and I have to use the avg, date and one other numerical column.
So I can analyze each jobname already knowing their pattern. I'm trying to separate each jobname with their information and then start to figure how to cluster them. If you have some idea, please share with me.