MINE: Free tool for detecting novel associations in large data sets

curiousepic

I was waiting for someone more knowledgeable to post something about this, but it's been a couple days and thought I'd bring it to LW's attention.

The maximal information coefficient (MIC) is a measure of two-variable dependence developed with the guidelines of generality and equitability in mind. The published paper describing MIC shows that it comes very close to achieving both goals simultaneously, and that it significantly outperforms competing methods in this regard.

Video summary

Main site with access to tool

I was waiting for someone more knowledgeable to post something about this, but it's been a couple days and thought I'd bring it to LW's attention.

The maximal information coefficient (MIC) is a measure of two-variable dependence developed with the guidelines of generality and equitability in mind. The published paper describing MIC shows that it comes very close to achieving both goals simultaneously, and that it significantly outperforms competing methods in this regard.

Video summary

Main site with access to tool

Check out figures S5.D and S6 from the SOM. If the relationship is functional (the linear, parabolic, sinusoidal cases on Figure S6), then the R2 calculated from LOESS regression is quite close to this MIC score, and that's not a coincidence. Of course LOESS R2 just dies when it encounters a non-functional relationship.

7

MINE: Free tool for detecting novel associations in large data sets

7

7

7

MINE: Free tool for detecting novel associations in large data sets

7

7