Implementing K-means Clustering Algorithm in MATLAB
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In this document, we will explore how to implement the K-means clustering algorithm using MATLAB. K-means clustering is a widely-used unsupervised learning algorithm designed to partition datasets into K distinct clusters. This algorithm helps reveal underlying patterns and structures by grouping similar data points together, enabling better understanding of data relationships. We will discuss the fundamental principles and step-by-step procedure of the K-means algorithm, and provide a practical example demonstrating its implementation in MATLAB. The implementation typically involves key functions like kmeans() for direct usage or custom code using centroid initialization, distance calculations (Euclidean distance), and iterative centroid updates. We'll also cover important considerations such as choosing the optimal K value using methods like the elbow method and handling initialization sensitivity. Let's begin our exploration!
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