The correct answer is A. k-means.
K-means is a clustering algorithm that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The k-means algorithm requires two parameters: the number of clusters k and an initial set of cluster centers. The algorithm proceeds by initializing k cluster centers, typically by selecting k observations at random. Then, each observation is assigned to the cluster with the nearest center. The cluster centers are then recomputed as the mean of the observations in each cluster. This process is repeated until the cluster centers no longer change.
K-means is a simple and efficient clustering algorithm that is often used as a baseline for comparison with other clustering algorithms. It is particularly well-suited for problems where the data is well-represented by a small number of clusters.
The other options are incorrect.
- B. k-mean is not a function. It is a typo of k-means.
- C. heatmap is a visualization technique that is used to represent data as a two-dimensional image. It is not a clustering algorithm.
- D. none of the mentioned is the correct answer.