Accelerated Mean Shift for Static and Streaming Environments

Daniel van der Ende, JMCM Thiery, E Eisemann

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Mean Shift is a well-known clustering algorithm that has attractive properties such as the ability to find non convex and local clusters even in high dimensional spaces, while remaining relatively insensitive to outliers. However, due to its poor computational performance, real-world applications are limited. In this
article, we propose a novel acceleration strategy for the traditional Mean Shift algorithm, along with a two-layer strategy, resulting in a considerable performance increase, while maintaining high cluster quality.We also show how to to find clusters in a streaming environment with bounded memory, in which queries need to be answered at interactive rates, and for which no mean shift-based algorithm currently exists. Our online structure is updated at very
minimal cost and as infrequently as possible, and we show how to detect the time at which an update needs to be triggered. Our technique is validated extensively in both static and streaming environments.
Original languageEnglish
Title of host publicationDATA ANALYTICS 2015
Subtitle of host publicationThe Fourth International Conference on Data Analytics
EditorsT Klemas, S Chan
PublisherInternational Academy, Research, and Industry Association ( IARIA )
Number of pages6
ISBN (Print)978-1-61208-423-7
Publication statusPublished - 2015
EventDATA ANALYTICS 2015, Nice, France - s.l.
Duration: 19 Jul 201524 Jul 2015


ConferenceDATA ANALYTICS 2015, Nice, France


  • Data stream clustering
  • Mean Shift

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