Which is the best method for clustering probability distributions?
You might fit a model separately for each unit to be clustered, or it may be appropriate to use a more sophisticated model such as a generalized linear mixed model. For step (2), you can then cluster based on these parameter estimates. At this stage you should have a small number of parameter estimates per unit.
How many data points are in a cluster?
Each data point (each object Obj that I want to cluster) actually literally contains 5 vectors of data. I know there are exactly 5 phases that these objects can be in. We’ll say (for the purposes of simplification) that each vector is of length N.
How to define proximity measures for data clustering?
Proximity Measures. For clustering, we need to define a proximity measure for two data points. Proximity here means how similar/dissimilar the samples are with respect to each other. Similarity measure S(xi,xk): large if xi,xk are similar. Dissimilarity(or distance) measure D(xi,xk): small if xi,xk are similar.
Which is a good balance between random points and clusters?
Since they are far from any meaningful cluster, each such point may end up being its own ‘cluster’. A good balance is K-Means++ variant [Arthur and Vassilvitskii, 2007], whose initialization will still pick random points, but with probability proportional to square distance from the previously assigned centroids.
How to cluster the parameters of a Weibull distribution?
I could then cluster the distributions based on differences in the two parameters of the Weibull distribution, lambda and k (scale and shape), probably normalized according to the variance of these parameters or something of the sort. This is the only case where I thought I might have an idea of how to normalize the parameters.
What does it mean to normalize values to sum 1?
What it means to calculate ratios involving negative values is open to interpretation. Maybe it’s a closer behavior to that of the softmax function in machine learning, as it makes all values positive and sum to 1. Just divide all the numbers through by 50. Should normalise it to the scale you want 0 – 1.