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

clusters

- 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

what %)of

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.