S-RASTER: contraction clustering for evolving data streams

S-RASTER: contraction clustering for evolving data streams

Abstract Contraction Clustering (RASTER) is a single-pass algorithm for density-based clustering of 2D data. It can process arbitrary amounts of data in linear time and in constant memory, quickly identifying approximate clusters. It also exhibits good scalability in the presence of multiple CPU cor...

Full description

Bibliographic Details
Main Authors: Gregor Ulm, Simon Smith, Adrian Nilsson, Emil Gustavsson, Mats Jirstrand
Format: Article
Language:English
Published: SpringerOpen 2020-08-01
Series:Journal of Big Data
Subjects:
Big data
Stream processing
Clustering
Machine learning
Unsupervised learning
Big data analytics
Online Access:http://link.springer.com/article/10.1186/s40537-020-00336-3

Internet

http://link.springer.com/article/10.1186/s40537-020-00336-3

Similar Items