What is the main difference between K-means and K-medoids clustering?

₹0.0

Question: What is the main difference between K-means and K-medoids clustering?

Options:

K-means uses centroids, while K-medoids uses actual data points
K-medoids is faster than K-means
K-means can only handle numerical data, while K-medoids can handle categorical data
K-medoids requires the number of clusters to be specified, while K-means does not

Correct Answer: K-means uses centroids, while K-medoids uses actual data points

Solution:

K-means uses centroids to represent clusters, while K-medoids uses actual data points as the center of clusters, making it more robust to outliers.

What is the main difference between K-means and K-medoids clustering?

K-means uses centroids, while K-medoids uses actual data points
K-medoids is faster than K-means
K-means can only handle numerical data, while K-medoids can handle categorical data
K-medoids requires the number of clusters to be specified, while K-means does not

What is the main difference between K-means and K-medoids clustering?

Correct Answer: K-means uses centroids, K-medoids uses actual data points.

Step 1: Understand that both K-means and K-medoids are clustering methods used to group similar data points.
Step 2: Learn that K-means uses a 'centroid' which is the average of all points in a cluster to represent that cluster.
Step 3: Realize that K-medoids uses an actual data point from the cluster, called a 'medoid', as the center of the cluster.
Step 4: Note that because K-medoids uses real data points, it is less affected by outliers (extreme values) compared to K-means.

No concepts available.