What is the main challenge when using K-means clustering on high-dimensional dat

What is the main challenge when using K-means clustering on high-dimensional data?

What is the main challenge when using K-means clustering on high-dimensional data?

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.

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