Two-dimensional visualization of k-means clustering algorithm

Data points
data point(s)
every millisecond(s)

Explanation of K-Means Clustering :

K-Means is one of the simplest unsupervised learning algorithms that solves the clustering problem. It groups all the objects in such a way that objects in the same group (group is a cluster) are more similar (in some sense) to each other than to those in other groups. For example, assume you have an image with a red ball on the green grass. K-Means will split all pixels into two clusters. The first cluster will contain the pixels of the ball, the second cluster will contain the pixels of the grass.IRIS Dataset is a table that contains several features of iris flowers of 3 species. Species can be "Iris-setosa", "Iris-versicolor", and "Iris-virginica". Each flower contains 5 features: Petal Length, Petal Width, Sepal Length, Sepal Width, and Species.