What is the definition of K-means clustering?

Conditions

• Data must be partitionable into distinct groups.
• Groups are non-overlapping.

Reasoning, step by step

1. Identify the algorithm as a clustering method.
2. State its primary function: partitioning data.
3. Specify the nature of the groups: distinct and non-overlapping.
4. Mention the purpose: uncovering hidden patterns or rethinking categories.

Example

The video defines K-means as a popular algorithm used to partition data into distinct, non-overlapping groups, which can help uncover hidden patterns or rethink existing categories.

Common misconceptions

• Assuming K-means creates overlapping groups.
• Thinking K-means is a supervised learning algorithm.

Watch the explanation

Explore next

• How do you run K-means clustering in R and specify the number of clusters?
• What information is contained in the object returned by the R kmeans function?
• How can you visualize K-means clusters and their centers using a scatter plot in R?