The K in K-means represents the number of clusters to be formed. When using this technique, the first step is to specify the desired number of clusters. This parameter is usually determined based on prior knowledge or through techniques such as the elbow method.
The Clustering Process
The K-means clustering process can be summarized in the following steps:
- Initialization: Randomly select K data points as initial cluster centroids.
- Assignment: For each data point, calculate the Euclidean distance to each centroid and assign it to the nearest cluster.
- Update: Recalculate the centroids of the clusters by taking the mean value of all data points assigned to each cluster.
- Repeat: Repeat steps 2 and 3 until the centroids no longer change or a maximum number of iterations is reached.
The distance between data points and cluster centroids is a crucial component of the K-means clustering algorithm. The most common distance metric used is the Euclidean distance, which calculates the straight-line distance between two points in a multidimensional space. However, other distance metrics, such as…