The Within-Cluster Sum of Squares (WCSS) is a measure used to evaluate the compactness of clusters in a clustering algorithm, such as k-means. It calculates the sum of squared distances between each data point and the centroid (mean) of the cluster it belongs to.
Mathematically, the WCSS for a set of clusters is expressed as:
WCSS (Within-Cluster Sum of Squares) and DBI (Davies–Bouldin Index) are both metrics used to evaluate clustering performance, but they focus on different aspects:
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Objective:
WCSS Measures compactness of clusters (intra-cluster similarity)
DBI Balances compactness and separation between clusters (inter-cluster dissimilarity)
Key Differences:
WCSS looks only within clusters.
DBI looks both within and between clusters.
