K-Means Clustering | Machine Learning | Scikit learn | Explained in Sinhala | Part 2
Channel: Wizard LK
Date: 2020-09-23

K-Means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Elbow Method 
In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use.

Silhouette Coefficient
Silhouette refers to a method of interpretation and validation of consistency within clusters of data. The technique provides a succinct graphical representation of how well each object has been classified. The silhouette value is a measure of how similar an object is to its own cluster compared to other clusters

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Code for the video:
https://colab.research.google.com/drive/19ubtEiBM6Z9cjDEpKvlcnKj7bTLDU9xk

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#KMeans #MachineLearning #Clustering