What is the significance of 'distance measures' in cluster analysis?
- Distance measures determine the similarities or differences between data points
- Distance measures help in determining the shape of the clusters
- Distance measures help in visualizing the clusters
- Distance measures indicate the number of clusters
Distance measures, like Euclidean distance or Manhattan distance, play a crucial role in cluster analysis. They determine the similarities or differences between data points. They influence how the clusters will be formed, as the most similar or closest data points get clustered together.
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