What is the main challenge when using K-means clustering on high-dimensional data?
Practice Questions
1 question
Q1
What is the main challenge when using K-means clustering on high-dimensional data?
Curse of dimensionality
Inability to handle categorical data
Difficulty in initializing centroids
Slow convergence
The curse of dimensionality makes it difficult for K-means to find meaningful clusters as the distance between points becomes less informative in high dimensions.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the main challenge when using K-means clustering on high-dimensional data?
Solution: The curse of dimensionality makes it difficult for K-means to find meaningful clusters as the distance between points becomes less informative in high dimensions.
Steps: 6
Step 1: Understand what K-means clustering is. It is a method used to group similar data points together.
Step 2: Know that K-means relies on measuring distances between data points to form clusters.
Step 3: Recognize that in high-dimensional data, there are many features (dimensions) for each data point.
Step 4: Realize that as the number of dimensions increases, the distance between points becomes less meaningful.
Step 5: Understand that this phenomenon is called the 'curse of dimensionality.'
Step 6: Conclude that because distances are less informative, K-means struggles to find clear and meaningful clusters in high-dimensional data.
