What is the effect of outliers on K-means clustering?

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Q: What is the effect of outliers on K-means clustering?

Solution: Outliers can significantly distort the cluster centroids in K-means clustering, leading to inaccurate clustering results.

Steps: 6

Step 1: Understand what K-means clustering is. It groups data points into clusters based on their similarities.
Step 2: Know what outliers are. Outliers are data points that are very different from the rest of the data.
Step 3: Realize that K-means uses the average of data points in a cluster to find the center, called the centroid.
Step 4: Understand that if an outlier is present, it can pull the centroid away from the main group of data points.
Step 5: Recognize that this distortion can lead to clusters that do not accurately represent the data.
Step 6: Conclude that outliers can make K-means clustering results less reliable.