How to Use K-Means Clustering to Solve Real-World Problems
K-means clustering is one of the simplest and most widely used unsupervised machine learning algorithms. The primary goal is to divide a set of points into different groups based on their features. Imagine you’re a city planner trying to understand neighborhoods or a marketer segmenting customers; K-means can be your go-to tool.
The objective is to minimize the sum of the squared differences between the data points and their respective cluster centroids. Mathematically, this is often represented as:
Random Initialization: K-Means begins by randomly selecting K initial cluster centroids (points that represent the centers of the clusters). These centroids can be any data points from the dataset or randomly generated points within the data’s range.
Assigning Data Points to Clusters: Each data point in the dataset is assigned to the nearest centroid. The proximity is typically measured using the Euclidean distance formula, but other distance metrics can also be used. The data point is assigned to the cluster represented by the nearest centroid.
Updating Cluster Centroids: After assigning all data points to clusters, the algorithm calculates the new centroids for each cluster. The new centroid is the mean (average) of all data points currently assigned to that cluster. This step moves the centroids closer to the center of their respective clusters.
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