Robust Scalable Visualized Clustering in Vector and Non Vector Semi-Metric Spaces

We describe an approach to data analytics on large systems using a suite of robust parallel algorithms running on both clouds and HPC systems. We apply this to cases where the data is defined in a vector space and when only pairwise distances between points are defined. We introduce improvements to known algorithms for functionality, features and performance but review state of the art as this is not broadly familiar. Visualization is valuable for steering complex analytics and we discuss it for both the non vector semimetric case and for clustering high dimension vector spaces. We exploit deterministic annealing which is heuristic but has clear general principles that can give reasonably fast robust algorithms. We apply methods to several life sciences applications.