|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'501'711
Articles rated: 2609
20 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
S-RASTER: Contraction Clustering for Evolving Data Streams | | Gregor Ulm
; Simon Smith
; Adrian Nilsson
; Emil Gustavsson
; Mats Jirstrand
; | | Date: |
21 Nov 2019 | | 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 therefore 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. | | Source: | arXiv, 1911.9447 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER