K — means clustering is a centroid-based unsupervised machine learning algorithm. Unsupervised learning uses the machine learning algorithm to analyze unlabelled data and find hidden patterns without human intervention. It’s clear from the name itself that K-means is a cluster-based algorithm.
➡️ Introduction to K-means Clustering
➡️ Types of Clustering:
❇️Centroid-based Clustering
❇️Hierarchical clustering
❇️Distribution-based Clustering
❇️Density-based Clustering
➡️ How K-Means works?
➡️ How to choose the optimal K value?
❇️Inertia & Elbow Method
❇️Silhouette Analysis
➡️ Image Segmentation using K-Means
➡️ Advantages & Disadvantages
➡️ Applications of K-Means
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