In which scenario would hierarchical clustering be preferred over K-means?

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Question: In which scenario would hierarchical clustering be preferred over K-means?

Options:

Correct Answer: When a hierarchy of clusters is desired

Solution:

Hierarchical clustering is preferred when a hierarchy of clusters is desired, as it provides a tree-like structure of the data.

In which scenario would hierarchical clustering be preferred over K-means?

In which scenario would hierarchical clustering be preferred over K-means?

Correct Answer: Jab hume clusters ka hierarchy chahiye hota hai.

Step 1: Understand what hierarchical clustering is. It groups data into a tree-like structure called a dendrogram.
Step 2: Know what K-means clustering is. It divides data into a fixed number of clusters (K) based on their similarities.
Step 3: Identify when you need a hierarchy. If you want to see how clusters are related or nested within each other, use hierarchical clustering.
Step 4: Consider the size of your data. Hierarchical clustering can be slow with large datasets, while K-means is faster.
Step 5: Think about the number of clusters. If you don't know how many clusters you want, hierarchical clustering can help you decide by showing the tree structure.

Hierarchical Clustering – A clustering method that builds a hierarchy of clusters, allowing for a tree-like representation of data.
K-means Clustering – A partitioning method that divides data into a predefined number of clusters based on distance to centroids.
Cluster Hierarchy – The organization of clusters in a tree structure, which is useful for understanding relationships between data points.