Automatically Selecting Parameters for Graph-Based Clustering
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 automa...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/80407 |
| _version_ | 1848764213422456832 |
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| author | Callister, Ross |
| author_facet | Callister, Ross |
| author_sort | Callister, Ross |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:15:47Z |
| format | Thesis |
| id | curtin-20.500.11937-80407 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:15:47Z |
| publishDate | 2020 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-804072020-08-05T04:49:05Z Automatically Selecting Parameters for Graph-Based Clustering Callister, Ross 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. 2020 Thesis http://hdl.handle.net/20.500.11937/80407 Curtin University fulltext |
| spellingShingle | Callister, Ross Automatically Selecting Parameters for Graph-Based Clustering |
| title | Automatically Selecting Parameters for Graph-Based Clustering |
| title_full | Automatically Selecting Parameters for Graph-Based Clustering |
| title_fullStr | Automatically Selecting Parameters for Graph-Based Clustering |
| title_full_unstemmed | Automatically Selecting Parameters for Graph-Based Clustering |
| title_short | Automatically Selecting Parameters for Graph-Based Clustering |
| title_sort | automatically selecting parameters for graph-based clustering |
| url | http://hdl.handle.net/20.500.11937/80407 |