I am interested in some papers and reports about analysing the following problem: Assume, we have a stream of objects and a defined similarity/distance measure to calculate similarity/distance between single objects and group of objects. We construct a clustering in an incremental way. The problem is to decide when to close the stream and stop adding new objects - which would mean that none of new objects will change the structure of the clustering. In other words there should be some convergence point where we can say the out clustering is a good representation of grouping.
I am specially interested in hierarchical clustering methods as they allow to construct cluster hierarchy without specifying and fixing the number of clusters during algorithm run.
I did some experiments that are based on comparing dendrograms over time using Bakers Gamma Index (http://rpackages.ianhowson.com/cran/dendextend/man/cor_bakers_gamma.html) and estimating the point where the correlation between past and present dendrogram is not changing up to some degree and then stop. Anyway it would be nice to compare this approach. I was looking for some papers with similar stated problems, but I did not succeed. Maybe someone can help?