How is K-Means Clustering Implemented?
K-means clustering is a popular unsupervised machine learning algorithm used to partition a dataset into distinct groups (clusters) based on similarity. The implementation involves several key steps:
- Initialization: Choose a predefined number of clusters, K. Randomly select K initial centroids from the dataset.
- Assignment Step: For each data point, calculate the distance to each centroid (using Euclidean distance, for instance) and assign the data point to the nearest centroid's cluster.
- Update Step: Recalculate the centroids by taking the mean of all data points assigned to each cluster. This results in a new centroid position for each cluster.
- Convergence Check: Repeat the assignment and update steps until the centroids no longer change significantly, or a maximum number of iterations is reached.
The K-means algorithm is efficient for large datasets but requires careful selection of K, as the outcome significantly affects the clustering results. Techniques like the elbow method can help determine the optimal number of clusters.