The correct answer is: B. Hierarchical
Hierarchical clustering is a type of clustering algorithm that groups data points into clusters based on their similarity. There are two main types of hierarchical clustering: agglomerative and divisive. Agglomerative clustering starts with each data point in its own cluster and then merges clusters until all data points are in one cluster. Divisive clustering starts with all data points in one cluster and then splits clusters until each data point is in its own cluster.
Merging approach is a technique used in hierarchical clustering to merge two clusters. The most common merging approach is single-linkage, which merges two clusters if the distance between the two closest data points in the clusters is less than a given threshold.
The other options are incorrect because they do not require merging approach.
- Partitional clustering is a type of clustering algorithm that divides data points into a fixed number of clusters. There are many different partitioning clustering algorithms, but they all work by iteratively assigning data points to clusters until each data point is in one cluster.
- Naive Bayes is a type of supervised machine learning algorithm that is used for classification. It works by assuming that the features of a data point are independent of each other and then calculates the probability that a data point belongs to each class based on the features of the data point.
- None of the mentioned is a type of clustering algorithm that requires merging approach.