Proximity measure We calculate the euclidean distance of each data point to its closest centroid We then compute the total sum of the squared distances, which is also known as the sum of the squared error( sse) A small value of sse means that the prototypes (centroids of this clustering are a better representation of the points in their cluster 8/20/2016 PATTERN RECOGNITIONProximity measure We calculate the Euclidean distance of each data point to its closest centroid. We then compute the total sum of the squared distances, which is also known as the sum of the squared error (SSE). A small value of SSE means that the prototypes (centroids) of this clustering are a better representation of the points in their cluster. 8/20/2016 PATTERN RECOGNITION 22