Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Data Mining
- /
- How to apply chracteristics of data on another dat...

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

03-13-2011 05:17 PM

Hi,

I have a data set which has its characteristics (cluster, Strat,...) and each has proporation. how can select from another data set data has the same characteristics same as the first one?

I have a data set which has its characteristics (cluster, Strat,...) and each has proporation. how can select from another data set data has the same characteristics same as the first one?

Accepted Solutions

Solution

Monday

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

Monday

In general, this would not typically be a concern of a data mining problem since the methods and software are intended for large data sets. Sampling can be helpful when resources or limited or when the distributions in the data are not reflective of the population, but the goal is to have your training/validation/test sample be as representative as possible.

If you are sampling, however, one approach would be to cluster the first data set, score the second data set using the cluster solution obtained on the first, and then sample proportionally from the second data set based on the distribution of clusters in the first data set. You might consider stratifying on certain grouping variables (e.g. gender, location, etc...) to make the distribution as balanced as possible.

I hope this helps!

Doug

All Replies

Solution

Monday

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

Monday

In general, this would not typically be a concern of a data mining problem since the methods and software are intended for large data sets. Sampling can be helpful when resources or limited or when the distributions in the data are not reflective of the population, but the goal is to have your training/validation/test sample be as representative as possible.

If you are sampling, however, one approach would be to cluster the first data set, score the second data set using the cluster solution obtained on the first, and then sample proportionally from the second data set based on the distribution of clusters in the first data set. You might consider stratifying on certain grouping variables (e.g. gender, location, etc...) to make the distribution as balanced as possible.

I hope this helps!

Doug