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Understanding the concepts of inter-cluster and intra-cluster distances is crucial in the context of clustering, a fundamental task in data science. Clustering involves grouping data points into clusters so that points in the same cluster are more similar to each other than to those in other clusters. The effectiveness of a clustering algorithm can often be evaluated by examining these distance metrics:
Inter-cluster Distance:
Intra-cluster Distance:
Understanding these definitions and their implications is essential for evaluating clustering results and improving clustering algorithms.