Python Fundamentals
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3 min read·Oct 6, 2023--
Clustering is a fundamental technique in machine learning and data analysis. It’s used to group similar data points together, making it easier to understand the structure within a dataset. K-Means clustering is one of the most popular and straightforward clustering algorithms. In this comprehensive guide, we’ll explore K-Means clustering in detail, along with practical code examples in Python.
K-Means is an unsupervised machine learning algorithm used for clustering. Its primary goal is to partition a dataset into groups, or “clusters,” such that data points in the same cluster are more similar to each other than to those in other clusters. Each cluster is represented by its center, known as a “centroid.”
Here’s a high-level overview of how K-Means works:
Initialization: Choose the number of clusters (K) and randomly initialize K centroids in the feature space.Assignment: Assign each data point to the nearest centroid, forming K clusters.Update Centroids: Recalculate the centroids by taking the mean of all data points assigned to each cluster.Repeat: Iteratively repeat steps 2 and 3 until convergence. Convergence occurs…