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S-RASTER: Contraction Clustering for Evolving Data Streams (c) 2016 - 2020 Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC), Gothenburg, Sweden Research and development by Gregor Ulm, Simon Smith, Adrian Nilsson, Emil Gustavsson, and Mats Jirstrand. This repository contains artifacts related to our paper "S-RASTER: Contraction Clustering for Evolving Data Streams." The content is as follows: \benchmark Copy of our internal benchmark setup, including an implementation of S-RASTER for use in the standard stream processing benchmarking utility 'rstream'. The provided scripts make it possible to reproduce the results presented in our paper. The input data need to be generated with the provided data generator first (see below). \data_generator Python script for generating synthetic data containing dense clusters that are spread out on a 2D canvas. \raster_py: Complete reference implementation of RASTER for batch data, written in Python. \sraster_kt: Prototypal implementations of the three nodes described in our paper on RASTER for evolving data streams (S-RASTER), i.e. 'projection', 'accumulation', and 'clustering'. This is not a complete implementation but nonetheless helpful for illustrating how our algorithm works.
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