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arClustering | K-Means| Day (9/45) | A2Z ML | Mohd Saqib
Clustering is one of the most fascinating concepts in machine learning, where the goal is to group similar data points together based on their inherent patterns. Unlike classification, clustering works without predefined labels, making it an essential technique for exploring data, discovering hidden patterns, and segmenting datasets. Whether you’re analyzing customer behavior, identifying natural groupings in text, or even clustering images, clustering methods like K-Means are invaluable tools for any data scientist.
Let’s start with a foundational understanding of clustering, explore how it differs from classification, and implement K-Means Clustering with a practical example.
Classification vs. Clustering
Before diving into clustering, let’s distinguish it from classification — a common source of confusion for beginners.
Classification
Classification is a supervised learning task where the algorithm assigns a predefined label to a data point based on training data. Example: Predicting whether an email is “spam” or “not spam” based on labeled training data. Key Point: Labels are available during training.
