Combining and comparing clustering and layout algorithms

Many clustering and layout techniques have been used for structuring and visualising complex data. This paper explores a number of combinations and variants of sampling, K-means clustering and spring models in making such layouts, using Chalmers’ 1996 linear iteration time spring model as a benchmark. This algorithm runs in O(N) time overall, but the run times for the new algorithms we describe reach O(N√N). We compare their layout quality and run times in laying out two collections of synthetic data, drawing samples from each collection of sizes ranging from 1000 to 20000. Based on these comparisons, we outline a number of avenues for future work that may further reduce time complexity and improve layout quality. ____________________________________________________________________________________________________