The correct answer is True.
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:
- Agglomerative : This is a “bottom up” approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
- Divisive : This is a “top down” approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
Hierarchical clustering is often used for exploratory data analysis, as it can provide a useful way to visualize the relationships between data points. However, it is important to note that hierarchical clustering does not provide a unique solution: different algorithms can produce different hierarchies, and the choice of which algorithm to use can have a significant impact on the results.
In addition, hierarchical clustering is not always robust to noise in the data. If there are outliers or other errors in the data, they can have a disproportionate impact on the results of the clustering.
Overall, hierarchical clustering is a powerful tool for exploratory data analysis, but it is important to be aware of its limitations.