Clustering aims to group similar data points. One of the most widely used algorithms for this task is K-Means. It's popular because it's relatively simple to understand and implement, yet often provides good results.
The core idea behind K-Means is to partition a dataset into a pre-determined number of clusters, denoted by the letter . The algorithm aims to make these clusters as distinct and compact as possible. It does this by iteratively finding cluster centers, called centroids, and assigning each data point to the nearest centroid.
Think of a centroid as the geometric center (or arithmetic mean) of all the points belonging to a particular cluster. The K-Means algorithm works through an iterative process:
Initialization: First, you must decide how many clusters you want to find, which is the value . Then, the algorithm initializes centroids. A common way to do this is to randomly select data points from your dataset and designate them as the initial centroids. Other initialization methods exist, but random selection is a straightforward starting point.
Assignment Step: For each data point in your dataset, calculate its distance to each of the centroids. Assign the data point to the cluster whose centroid is the nearest (closest). The most common way to measure distance here is the standard Euclidean distance (the straight-line distance you'd measure with a ruler), but the concept is simply "closest centroid". After this step, every data point belongs to one of the clusters.
Update Step: Recalculate the position of each of the centroids. The new position for a centroid is the mean (average position) of all the data points assigned to its cluster in the previous step. If a cluster ends up with no points assigned to it (which can happen, especially with poor initialization), the centroid might be removed or randomly reassigned, though typically it stays put until points are assigned in a later iteration.
Iteration: Repeat the Assignment Step and the Update Step until a stopping condition is met. Common stopping conditions include:
When the algorithm stops, the centroids represent the centers of the final clusters, and each data point belongs to the cluster associated with the nearest final centroid.
Imagine you have scattered data points on a 2D plot, and you choose .
The following chart shows a possible final state after running K-Means with on some sample 2D data. The points are colored according to their assigned cluster, and the larger markers indicate the final positions of the centroids.
Example of a dataset partitioned into three clusters using K-Means. Each color represents a different cluster, and the crosses mark the final centroid locations.
Under the hood, K-Means tries to minimize an objective function called the Within-Cluster Sum of Squares (WCSS). This sounds technical, but the intuition is simple: it measures the total squared distance between each data point and the centroid of its assigned cluster. By minimizing WCSS, K-Means tries to make the clusters as compact (points close to their centroid) and separated as possible. Each iteration of the assignment and update steps typically reduces the WCSS, leading towards a good (though not always the absolute best possible) clustering solution.
K-Means is a foundational algorithm for partitioning data when you don't have labels. Its simplicity and speed make it a great first choice for many clustering tasks. However, as we'll see next, choosing the right value for and understanding the algorithm's limitations are important considerations.
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