Scalable parallel minimum spanning forest computation
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
A minimum spanning tree-inspired clustering-based outlier detection technique
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
Dense subgraph mining with a mixed graph model
Pattern Recognition Letters
Clustering based on a near neighbor graph and a grid cell graph
Journal of Intelligent Information Systems
Enhancing minimum spanning tree-based clustering by removing density-based outliers
Digital Signal Processing
Hi-index | 0.00 |
Due to their ability to detect clusters with irregular boundaries, minimum spanning tree-based clustering algorithms have been widely used in practice. However, in such clustering algorithms, the search for nearest neighbor in the construction of minimum spanning trees is the main source of computation and the standard solutions take O(N^{2}) time. In this paper, we present a fast minimum spanning tree-inspired clustering algorithm, which, by using an efficient implementation of the cut and the cycle property of the minimum spanning trees, can have much better performance than O(N^{2}).