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Clustering is an essential data mining tool for analyzing and groupingsimilar objects. In big data applications, however, many clustering algorithmsare infeasible due to their high memory requirements and/or unfavorable runtimecomplexity. In contrast, Contraction Clustering (RASTER) is a single-passalgorithm for identifying density-based clusters with linear time complexity.Due to its favorable runtime and the fact that its memory requirements areconstant, this algorithm is highly suitable for big data applications where theamount of data to be processed is huge. It consists of two steps: (1) acontraction step which projects objects onto tiles and (2) an agglomerationstep which groups tiles into clusters. This algorithm is extremely fast in bothsequential and parallel execution. Our quantitative evaluation shows that asequential implementation of RASTER performs significantly better than variousstandard clustering algorithms. Furthermore, the parallel speedup issignificant: on a contemporary workstation, an implementation in Rust processesa batch of 500 million points with 1 million clusters in less than 50 secondson one core. With 8 cores, the algorithm is about four times faster.

Contraction Clustering (RASTER): A Very Fast Big Data Algorithm for  Sequential and Parallel Density-Based Clustering in Linear Time, Constant  Memory, and a Single Pass