Inter-Cluster vs. Intra-Cluster Distances

Requirements Clarification & Assessment

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:

  • Definition: The distance between the centroids (or representative points) of different clusters.
  • Purpose: Measures how distinct or separate different clusters are. Ideally, this distance should be maximized to ensure that clusters are well-separated.

• Intra-cluster Distance:

  • Definition: The distance between points within the same cluster.
  • Purpose: Measures how compact the clusters are. Ideally, this distance should be minimized to ensure that points within a cluster are close to each other.

Understanding these definitions and their implications is essential for evaluating clustering results and improving clustering algorithms.