S-RASTER: Contraction Clustering for Evolving Data Streams

Authors:

Preprint

4 February 2020

preprint
Published by arXiv in arXiv

DOI:https://doi.org/10.48550/arXiv.1911.09447

Abstract

Contraction Clustering (RASTER) is a very fast algorithm for density-based clustering, which requires only a single pass. 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 cores. Yet, RASTER is limited to batch processing. In contrast, S-RASTER is an adaptation of RASTER to the stream processing paradigm that is able to identify clusters in evolving data streams. This algorithm retains the main benefits of its parent algorithm, i.e. single-pass linear time cost and constant memory requirements for each discrete time step in the sliding window. The sliding window is efficiently pruned, and clustering is still performed in linear time. Like RASTER, S-RASTER trades off an often negligible amount of precision for speed. It is very well suited to real-world scenarios where clustering does not happen continually but only periodically. We describe the algorithm, including a discussion of implementation details.

Keywords

All Related Versions

This publication has 3 references indexed in Scilit:

Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery From Data, 2009
Automatic Subspace Clustering of High Dimensional Data
Data Mining and Knowledge Discovery, 2005
Survey of Clustering Algorithms
IEEE Transactions on Neural Networks, 2005