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    Automatically Selecting Parameters for Graph-Based Clustering

    Callister R 2020.pdf (4.626Mb)
    Access Status
    Open access
    Authors
    Callister, Ross
    Date
    2020
    Supervisor
    Mihai Lazarescu
    Sonny Pham
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/80407
    Collection
    • Curtin Theses
    Abstract

    Data streams present a number of challenges, caused by change in stream concepts over time. In this thesis we present a novel method for detection of concept drift within data streams by analysing geometric features of the clustering algorithm, RepStream. Further, we present novel methods for automatically adjusting critical input parameters over time, and generating self-organising nearest-neighbour graphs, improving robustness and decreasing the need to domain-specific knowledge in the face of stream evolution.

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