clustering - K-means: Why minimizing WCSS is maximizing Distance ...
However, from a mathematical standpoint, I don't understand why minimizing the WCSS (within-cluster sums of squares) will necessarily maximize the distance between clusters. In other words, can …
kMeans - acceptable value for WCSS - Cross Validated
40 clusters -> 145,6 wcss, but i 'dislike' a good wcss value with a subpar count of clusters. using em helps to compare the clustering results.
What does minimising the loss function mean in k-means clustering?
Sep 17, 2020 · The algorithm tries to minimise the within-cluster sum of squares (WCSS) value which is a measure of the variance within the clusters. However, I am having trouble understanding what is …
Statistical test for comparing number of clusters in data
Mar 11, 2023 · The way the algorithm proceeds, it doesn't leave room for computing a log likelihood without making some very strong assumptions that likely don't hold. i tried going that route and …
Can the elbow method be used in PCA (Principal ... - Cross Validated
May 16, 2025 · The elbow method is commonly used with K-means clustering to determine the optimal number of clusters by plotting the within-cluster sum of squares (WCSS) against the number of …
The number of clusters in the K-means and the within-cluster SS
Sep 1, 2021 · Preamble: You talk about the "underlying true clusters", but in applied clustering this is a highly problematic concept. Assuming a certain model, one can define what is meant by "true …
What does total ss and between ss mean in k-means clustering?
Jan 19, 2014 · I'm very new to cluster analysis. I'm using R for k-means clustering and I wonder what those things are. And what is better if their ratio is smaller or larger?
SSB - Sum of squares between clusters - Cross Validated
I got a little confused with the squares and the sums. As far as I know, the variance or total sum of squares (TSS) is smth like $\sum_ {i}^ {n} (x_i - \bar x)^2$ and the sum of squares within (SSW...
TSS returned by K means clustering is always the same
Sep 19, 2015 · Total sum of squares is literally the total, every point to every other point. Unless you change the data set, this is not expected to change. What you are interested in is WCSS, within …
clustering - Why is the k-means algorithm minimizing the within cluster ...
Minimizing within-cluster variance automatically maximizes between-cluster variance. Although this duality does not necessarily hold for other measures of similarity/dissimilarity, it motivates the …