What is elbow method?:
Elbow method So elbowing is this mechanism of
social reiforcement/communication about something that is generally considered bad to say
aloud or is too subtle to try to find words for.
Okay, just kidding, while that’s kinda true, I was just pranking on y’all. What I want to
talk about is a stats/math/Machine Learning method used when trying to find clusters in a
given dataset. So [Elbow Method] (https://en.wikipedia.org/wiki/Elbow_method_(clustering))
is basically a measure/method for interpretation and validation of conistency of a cluster.
Ugh.. the original sentence in Wikipedia is so long with all 10-letter words, I couldn’t
even type it again.(Above attempt was simplified during typing-on-the-fly)
The basic issue is that, during a cluster analysis we need to settle on a few things:
* A measure for distance within, across and between clusters and points in the
- A method/algorithm for updating, re-assigning the points to clusters.
- Optional: A formula for guessing the number of algorithms. In most cases this is
optional, and parameterized.
In the case of elbow method it is a visual method for the third option. Basically, it’s a
ratio of variance (within clusters) divided by overall variance. So it explains how much(or
the total variance is explained by choosing “n” number of clusters.
The name elbow method comes from visually plotting the number of clusters Vs the ratio(% of
variance explained) and finding that point where there’s an acute bend(if no.of.clusters is
in X-axis), picking the number of clusters at that point.