K-mean is a partitioning technique in which objects are categorized into K groups. In this algorithm, the clusters are spherical with the data points are aligned around that cluster, and the variance of the clusters is similar to one another.
The K-means algorithm is a popular unsupervised machine learning algorithm used for clustering data points into groups or clusters based on their similarity. Here’s a breakdown of how the algorithm works:
- Initialization: Choose K initial centroids randomly from the data points. K represents the number of clusters you want to create.
- Assignment: Assign each data point to the nearest centroid, forming K clusters.
- Update Centroids: Recalculate the centroids of the clusters by taking the mean of all data points assigned to each cluster.
- Repeat: Repeat steps 2 and 3 until convergence, meaning that the centroids no longer change significantly or a specified number of iterations is reached.
The algorithm aims to minimize the within-cluster variance, also known as inertia or sum of squared distances, by iteratively optimizing the placement of centroids. K-means is sensitive to the initial placement of centroids and may converge to a local minimum, so it’s common to run the algorithm multiple times with different initializations and choose the result with the lowest inertia.
K-means is widely used in various applications such as customer segmentation, image compression, anomaly detection, and more. However, it has some limitations, such as the need to specify the number of clusters beforehand and its sensitivity to outliers.