K-Means Clustering

What is K-means Clustering? K-means Clustering Explained

K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into K distinct clusters based on their similarity. It aims to group similar data points together and separate dissimilar ones by minimizing the within-cluster sum of squares.

The algorithm works as follows:

Initial centroid assignment: Randomly select K data points from the dataset as the initial centroids, which represent the centers of the clusters.

Assignment step: For each data point, calculate the Euclidean distance (or other distance metric) to each centroid. Assign the data point to the cluster with the closest centroid.

Update step: Recalculate the centroids for each cluster by taking the mean of all data points assigned to that cluster. This moves the centroids to the center of their respective clusters.

Repeat steps 2 and 3: Repeat the assignment and update steps iteratively until convergence. Convergence occurs when the centroids no longer change significantly or when a maximum number of iterations is reached.

Final clustering: Once convergence is achieved, the algorithm outputs the final clustering, where each data point belongs to a specific cluster based on its distance to the nearest centroid.

The K-means algorithm aims to minimize the within-cluster sum of squares, which is the sum of the squared distances between each data point and its centroid within its assigned cluster. The algorithm seeks to find the optimal centroids that minimize this sum.

However, K-means has some limitations. It assumes that the clusters are spherical, isotropic, and have similar sizes, which may not always hold true. It is also sensitive to the initial centroid selection, as it may converge to a suboptimal solution. Therefore, it is common to run K-means multiple times with different initializations to mitigate this issue.

K-means clustering has various applications, including customer segmentation, image compression, document clustering, anomaly detection, and pattern recognition. It is a popular and widely used algorithm due to its simplicity and efficiency.