kNN-MST-Agglomerative: A fast and scalable graph-based data clustering approach on GPU

Data clustering is a distinctive method for analyzing complex networks in terms of functional relationships of the comprising elements. A number of graph-based algorithms have been proposed so far to tackle the complexity of the problem and many of them are based on the representation of data in the form of a minimum spanning tree (MST). In this work, we propose a graph-based agglomerative clustering method that is based the k-Nearest Neighbor (kNN) graphs and the Borůvka's-MST Algorithm, (termed as, kNN-MST-Agglomerative). The proposed method is inherently parallel and in addition it is applicable to a wide class of practical problems involving large datasets. We demonstrate the performance of our method on a set of real-world biological networks constructed from a renowned breast cancer study.