K-Means Clustering Implementation Steps: 1 to 3
Explore the initial steps to implement k-means clustering, including importing libraries, creating an artificial dataset with make_blobs, and setting up the algorithm to cluster data points into groups. Understand how centroids update to form natural clusters through this hands-on exercise.
We'll cover the following...
We'll cover the following...
Quick overview of k-means clustering
Another popular technique to reduce data complexity is k-means clustering, which identifies groups of data points without prior knowledge of existing classes.
K-means clustering splits the dataset into k number of clusters, with k representing the number of clusters. Setting k to “3,” for example, splits the data into three clusters.
Each ...