Notes

In clustering, the sum squared error (SSE) is also known as the within-cluster sum of squares (WCSS) and it is a criterion used to evaluate the performance of clustering algorithms. It measures the sum of the squared distances between each data point and the centroid of the cluster to which it belongs. The smaller the SSE, the better the clustering solution.

The equation for the SSE in clustering is:

SSE = Σ(i=1 to k) Σ(x in C_i) ||x - μ_i||^2

Where k is the number of clusters, C_i is the i-th cluster, x is a data point, and μ_i is the centroid of the i-th cluster.

The goal of clustering algorithms is to minimize the SSE by finding the best partitioning of the data into clusters, such that the data points within each cluster are as similar as possible (i.e. have a small distance to the cluster centroid). The SSE is a commonly used metric to evaluate the quality of clustering algorithms and different algorithms are compared using this criterion.

In summary, the Sum Squared Error (SSE) or the Within-cluster sum of squares (WCSS) is a criterion used to evaluate the performance of clustering algorithms, it measures the sum of the squared distances between each data point and the centroid of the cluster to which it belongs. The goal of clustering algorithms is to minimize the SSE by finding the best partitioning of the data into clusters, such that the data points within each cluster are as similar as possible.


The interpretation of the sum squared error (SSE) criterion in clustering is that it measures the overall similarity of the data points within each cluster. The smaller the SSE, the better the clustering solution.

A low SSE indicates that the data points within each cluster are similar to each other and that the clusters are well-defined. In other words, the points within a cluster are close to the centroid of that cluster, indicating that they are similar to each other.

A high SSE indicates that the data points within each cluster are dissimilar to each other and that the clusters are poorly defined. In other words, the points within a cluster are far from the centroid of that cluster, indicating that they are dissimilar to each other.

It's important to notice that minimizing the SSE is not the only goal of clustering, it's also important to consider other metrics such as the silhouette score, the Davies-Bouldin index and the Calinski-Harabasz index, which take into account the similarity between the points within a cluster and the dissimilarity between the points of different clusters.

In summary, the interpretation of the sum squared error (SSE) criterion in clustering is that it measures the overall similarity of the data points within each cluster, a low SSE indicates that the data points within each cluster are similar to each other and that the clusters are well-defined, a high SSE indicates that the data points within each cluster are dissimilar to each other and that the clusters are poorly defined. However, it's important to consider other metrics to evaluate the performance of clustering algorithms.