Print Email Facebook Twitter Accelerated Mean Shift for static and streaming environments Title Accelerated Mean Shift for static and streaming environments Author Van der Ende, D.J. Contributor Eisemann, E. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Intelligent Systems Programme Computer Graphics & Visualization Date 2015-03-31 Abstract 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 thesis, we propose a novel acceleration strategy for the traditional Mean Shift algorithm, along with a two-layers 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 can be answered at interactive rates, and for which no Mean Shift-based algorithm currently exists. Our online structure can be updated at very minimal cost and as infrequently as possible, and we show how to detect the time at which this update needs to be performed. Our technique is validated extensively in both static and streaming environments. Subject mean shiftdata stream clusteringdata mining To reference this document use: http://resolver.tudelft.nl/uuid:7fdb578a-a3e3-430c-b257-c85bfc45d3d9 Part of collection Student theses Document type master thesis Rights (c) 2015 Van der Ende, D.J. Files PDF Master_Thesis_Daniel_vd_Ende.pdf 4.72 MB Close viewer /islandora/object/uuid:7fdb578a-a3e3-430c-b257-c85bfc45d3d9/datastream/OBJ/view