K-Means is a popular machine learning algorithm used for clustering data points into groups. The algorithm works by grouping similar data points together based on their proximity in the feature space. Here we see scattered data points that we want to organize into three clusters, where K equals 3.
The first step in K-Means is initialization. We start by choosing the number of clusters K, which is 3 in our example. Then we randomly place 3 centroids in the data space. These centroids are shown as colored stars and represent the initial centers of our clusters. The red, blue, and green stars are our three initial centroids placed randomly among the data points.
In the assignment phase, we calculate the distance from each data point to all three centroids. Each point is then assigned to the nearest centroid. Watch as the gray points change color to match their assigned centroid. The dashed lines show the connection between each point and its assigned centroid, forming our initial clusters.
In the update phase, we calculate the mean position of all points assigned to each cluster. The centroids then move to these new positions, which better represent the center of their respective clusters. Watch as each colored star moves along the arrow to its new optimal position based on the average location of its assigned points.
To summarize K-Means clustering: This algorithm groups data into K clusters by iteratively assigning points to the nearest centroid and updating centroid positions. The process continues until the centroids stabilize, creating well-defined clusters based on data proximity. K-Means is widely used in machine learning for pattern recognition, market segmentation, and data analysis.