Advantages and Disadvantages of Hierarchical Clustering
Looking for advantages and disadvantages of Hierarchical Clustering?
We have collected some solid points that will help you understand the pros and cons of Hierarchical Clustering in detail.
But first, let’s understand the topic:
What is Hierarchical Clustering?
Hierarchical clustering is a way to group similar things together. Imagine sorting your toys into piles based on color, size or shape. This method does the same with data, making groups and sub-groups that share common features.
What are the advantages and disadvantages of Hierarchical Clustering
The following are the advantages and disadvantages of Hierarchical Clustering:
| Advantages | Disadvantages |
|---|---|
| Easy to understand and implement | Can’t handle large datasets |
| No need for predefined clusters | Sensitive to noise and outliers |
| Provides a visual representation | No undo in clustering process |
| Captures hierarchical relationships | Assumes data as hierarchical |
| Can work with any distance measure. | Hard to determine optimal clusters |
Advantages of Hierarchical Clustering
- Easy to understand and implement – Hierarchical Clustering is simple to grasp and apply. It’s a straightforward process that doesn’t require a lot of technical knowledge.
- No need for predefined clusters – It doesn’t require us to know the number of clusters beforehand, which makes it flexible.
- Provides a visual representation – It offers a visual output, known as a dendrogram, which helps in understanding the clustering process better.
- Captures hierarchical relationships – It’s great at identifying and showing hierarchical relationships between clusters, demonstrating how they are linked.
- Can work with any distance measure. – It’s versatile as it can work with any distance measure, allowing it to handle different types of data.
Disadvantages of Hierarchical Clustering
- Can’t handle large datasets – Hierarchical clustering struggles with big datasets, as it requires high computational power and time, making it less efficient for large-scale tasks.
- Sensitive to noise and outliers – It’s sensitive to noise and outliers, meaning a small change can significantly impact the final clusters, leading to less accurate results.
- No undo in clustering process – Unlike other clustering methods, once a decision is made to combine two clusters, it can’t be undone. This one-way process can lead to errors.
- Assumes data as hierarchical – This method assumes that the data is hierarchical in nature, which might not always be the case, limiting its applicability.
- Hard to determine optimal clusters – It’s challenging to identify the right number of clusters in hierarchical clustering, as the decision is often subjective and lacks a clear-cut criterion.
That’s it.
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