k-means is one of the simplest, most popular, and most well-known clustering algorithms. It is a kind of partitioning clustering method. It partitions input data by defining a random initial cluster center based on a given number of clusters. In the next iteration, it associates the data items to the nearest cluster center using Euclidean distance. In this algorithm, the initial cluster center can be chosen manually or randomly. k-means takes data and the number of clusters as input and performs the following steps:
- Select k random data items as the initial centers of clusters.
- Allocate the data items to the nearest cluster center.
- Select the new cluster center by averaging the values of other cluster items.
- Repeat steps 2 and 3 until there is no change in the clusters.
This algorithm aims to minimize the sum of squared errors:
k-means is one of the fastest and most robust algorithms of its kind. It works best with a dataset with distinct...