What is the main difference between K-means and hierarchical clustering?
Practice Questions
1 question
Q1
What is the main difference between K-means and hierarchical clustering?
K-means is a partitional method, while hierarchical is a divisive method
K-means requires the number of clusters to be defined, while hierarchical does not
K-means can only be used for numerical data, while hierarchical can handle categorical data
K-means is faster than hierarchical clustering for small datasets
K-means is a partitional clustering method that divides data into a fixed number of clusters, while hierarchical clustering builds a tree of clusters without needing to specify the number of clusters in advance.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the main difference between K-means and hierarchical clustering?
Solution: K-means is a partitional clustering method that divides data into a fixed number of clusters, while hierarchical clustering builds a tree of clusters without needing to specify the number of clusters in advance.
Steps: 3
Step 1: Understand that K-means is a method that groups data into a specific number of clusters that you choose beforehand.
Step 2: Realize that hierarchical clustering creates a tree-like structure of clusters, allowing you to see how clusters are related without needing to decide on the number of clusters first.
Step 3: Remember that K-means requires you to set the number of clusters (like 3 clusters), while hierarchical clustering can show you many clusters and how they connect.
