The ever increasing amount of data generated by scientific simulations coupled with system
I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are …

Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which
can play an important role in the understanding and organization of datasets. However, its …

In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK
algorithm on the GPU which requires only O (Nk) space and uses a parameter k to trade off …

Clustering algorithms are used to generate clusters of elements having similar
characteristics. Among the different groups of clustering algorithms, agglomerative algorithm …

Abstract The advent of Big Data has led to the rapid growth in the usage of parallel
clustering algorithms that work over distributed computing frameworks such as MPI …

We develop and extensively evaluate highly scalable distributed-memory algorithms for
computing minimum spanning trees (MSTs). At the heart of our solutions is a scalable …

Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm
over traditional partitioning-based clustering as it does not require the number of clusters as …

The increasing gap between available compute power and I/O capabilities is resulting in
simulation pipelines running on leadership computing facilities being reformulated. In …

Computing a Single-Linkage Dendrogram (SLD) is a key step in the classic single-linkage
hierarchical clustering algorithm. Given an input edge-weighted tree $ T $, the SLD of $ T …

The growing application of data-driven analytics in materials science has led to the
emergence and popularity of the relatively new field of materials informatics. Of the many …