k-Means Cluster Analysis

The k-Means cluster analysis is one of the simplest and most common procedures for cluster analysis. Thus, the k-Means method is one of the most widely used methods. It is a partitioning procedure that is particularly suitable for large amounts of data.
The k-means method tries to distribute the data points among the k clusters in such a way that the sum of the distances from each point to the respective cluster centroid is minimised.
And here is the online k-Means Cluster Calculator on DATAtab:
datatab.net/statistics-calcul...

[@lisandro-abulatif]

One of the best explanations I've found in KZbin about k-means!

[@aekphakiti6340]

Love all your videos. Very effective, clear and reliable results. Many thanks.

[@datatab]

Many thanks for your nice Feedback!!! Regards Hannah

[@destadereje8016]

Very clear and interesting explanation. Thank you, dear.

[@datatab]

Glad it was helpful!

[@zxph]

Great video. Thanks

[@user-kn2ge8wf8w]

Thanks a lot

[@danjarupath]

Very good knowledge 😊😊😊 thank you

[@datatab]

Most welcome 😊

[@barisguner3965]

Very easy to understand

[@datatab]

Thanks!

[@SuperHutomo]

Why did you decide to choose 2 instead of 3 clusters ?, to me the 3 clusters looked neater on graph.

[@pramitthapa283]

that's what most of the KZbin instructors do. Don't explain enough so that a novice could understand it.

[@pedrocoelho5562]

And is due to this that many groups then take the statistic and assume causation out of correlation as if it was a causation truth and not a result of the few variables being analysed.