his clustering approach initially assumes that each data instance represents a single cluster.

The correct answer is C. agglomerative clustering.

Agglomerative clustering is a hierarchical clustering method. It starts with each data point in its own cluster, and then merges the two most similar clusters repeatedly until all the data points are in one cluster.

The other options are:

• A. expectation maximization is an iterative algorithm for estimating the parameters of a mixture model. It is often used for clustering data.
• B. k-means clustering is a method of vector quantization, originally from signal processing, 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.
• D. conceptual clustering is a method of knowledge discovery that groups objects together based on their similarity. It is often used for data mining and machine learning.

I hope this helps!