Contents
How to calculate the size of a cluster?
As a start, you might try something like calculating the area of the convex hull of each cluster which will give you information about cluster size. Also dividing the number of samples in each cluster by the area of cluster’s convex hull you can get the measure of cluster density.
How is the density of a cluster expressed?
The “density” or “goodness” is expressed by the ratio of the “betweenss” – the sum of squares between the clusters – and the “totalss” which is the total sum of squares for the entire collection. The higher the ratio, the better. Here is a quick example in R: Thanks for contributing an answer to Cross Validated!
How to calculate the density of a centroid?
For comparison between datasets, you could also scale the final density scores by dividing each centroid’s score by the total across all centroids. Then each score would be a value between 0 and 1. K-Means tries to minimize the sum of square distances of the points to their cluster center.
Which is more dense TD or fxsa cluster?
I would like a way to show that TD is more dense than FXSA, as well as smaller in total size. Firstly, there are many distance measures and the distance of the samples to the group mean is a good measure of density of cluster in my opinion.
So requirement of cluster and equally sized groups can not be satisfied at the same time. There is a cleaner post-processing, given cluster centroids. Let N be the number of items, K the number of clusters and S = ceil (N/K) maximum cluster size.
How to sort centroids by descending size of clusters?
Sort the centroids by descending size of their associated clusters in an array. For i = 1 through k -1, push the data points in cluster i with minimal distance to any other centroid j ( i < j ≤ k) off to j and recompute the centroid i (but don’t recompute the cluster) until the cluster size is n / k.
What’s the algorithm for clustering coordinates into neighborhoods?
The algorithm used is k-means nearest neighbors with each neighborhood as a cluster center. For the purpose of this exercise, we’ll only work with a sample of 2000 rows for computational speed. We’ll also remove all features except longitude and latitude.
How to Group N points in k clusters of equal size?
Where x and y are double floating numbers, n can range from 100 to 10000 and k can range from 2 to 100. Also k is known before the algorithm runs. I started to resolve the problem by using the http://en.wikipedia.org/wiki/K-means_clustering algorithm, which work great and fast to produce exactly k clusters of roughly the same size.