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clustering - K-means: Why minimizing WCSS is maximizing Distance . . . From a conceptual and algorithmic standpoint, I understand how K-means works 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
kMeans - acceptable value for WCSS - Cross Validated $\begingroup$ chl: to answer briefly your questions - yes, i used it (kmeans of weka) on the same data set firstly and secondly, with all 21 attributes - different k arguments 'of course' -> bad wcss value afterwards weka kmeans was applied with three selected attributes using different arguments for k (in the range 2-10) however, using rapidminer (another data mining software) provided
What does minimising the loss function mean in k-means clustering? The centroids are then updated after the points are all assigned, and points are re-assigned again The algorithm continues to iterate until the clusters do not change anymore The algorithm tries to minimise the within-cluster sum of squares (WCSS) value which is a measure of the variance within the clusters
How to find the optimal number of clusters for spectral clustering . . . Now that you've figured out what WCSS is visually, you'll see that the WCSS is high at the beginning and you'll notice it drop substantially and then after a while, it will still drop but there won't be any substantial change That point where the last big drop is, that's the optimal number of clusters
r - What should be the ideal number of clusters for the plot whose . . . Furthermore, WCSS is expected to decrease with the number of clusters Even just assigning a single point to a new cluster obvioudly decreases WCSS, but foes not yield a better clustering So if you don't see a clear 'outlier' where the curve drops a lot and then abruptly stops dropping, then something did not work
r - Comparison of k-means clustering output - Cross Validated Hence when I give k=2, the output perfect matches with R's In fact, the output is perfect for k=3 and k=4 too (I use 'nstart' to get the best output) But for k=5 and above, the values are varying The total WCSS for R's kmeans for k=5 was 46 4 while the same for my algorithm was 49 4
Should I expect inertia from a K-Means solution on counts to be . . . Never compare WCSS across different data versions or data sets It's trivial to see that scaling all attributes by a factor of 2 does not affect the clustering, but changes the WCSS by a factor of 4 So you can arbitrarily inflate WCSS - or reduce them Just scale your data by 0 00001
What does total ss and between ss mean in k-means clustering? It's basically a measure of the goodness of the classification k-means has found SS obviously stands for Sum of Squares, so it's the usual decomposition of deviance in deviance "Between" and deviance "Within"