S-RASTER: Contraction Clustering for Evolving Data Streams

TL;DR

S-RASTER is a fast, memory-efficient streaming clustering algorithm that adapts the RASTER density-based method for evolving data streams, enabling real-time cluster detection with minimal precision loss.

Contribution

It introduces S-RASTER, a novel adaptation of RASTER for streaming data, maintaining linear time and constant memory per time step while effectively handling evolving clusters.

Findings

S-RASTER is at least 50% faster than competing algorithms.

It achieves good qualitative clustering results on standard metrics.

The algorithm is well-suited for real-world periodic clustering scenarios.

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 cores. RASTER exhibits very competitive performance compared to standard clustering algorithms, but at the cost of decreased precision. Yet, RASTER is limited to batch processing and unable to identify clusters that only exist temporarily. 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 within a 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. Our evaluation shows that competing algorithms are at least 50% slower. Furthermore, S-RASTER shows good qualitative results, based on standard metrics. It is very well suited to real-world scenarios where clustering does not happen continually but only periodically.