Mining high-dimensional and graph data using spectral analysis
Although the field of data mining has seen major advancements in the past fifteen years, algorithms for handling complex data (with high dimensionality or complex graph structures) are only becoming the mainstream in recent years. To address the difficulties of mining complex data, we argue that a r...
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| Бусад зохиолчид: | |
| Формат: | Дипломын ажил |
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2008
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| Онлайн хандалт: | https://hdl.handle.net/10356/2360 |
| _version_ | 1826110821005000704 |
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| author | Li, Wenyuan |
| author2 | Ng Wee Keong |
| author_facet | Ng Wee Keong Li, Wenyuan |
| author_sort | Li, Wenyuan |
| collection | NTU |
| description | Although the field of data mining has seen major advancements in the past fifteen years, algorithms for handling complex data (with high dimensionality or complex graph structures) are only becoming the mainstream in recent years. To address the difficulties of mining complex data, we argue that a right understanding of data characteristics (i.e., the general information of the data that is not particularly designed for any specific data mining task, but might enhance many types of data mining tasks) is important. The objective of this thesis is to study and exploit spectral information to provide quick insights into how data characteristics are beneficial to specific applications. We study issues concerning the design of how spectral information can be integrated into the needs of different types of analysis. |
| first_indexed | 2024-10-01T02:40:26Z |
| format | Thesis |
| id | ntu-10356/2360 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T02:40:26Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/23602023-03-04T00:39:15Z Mining high-dimensional and graph data using spectral analysis Li, Wenyuan Ng Wee Keong School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval Although the field of data mining has seen major advancements in the past fifteen years, algorithms for handling complex data (with high dimensionality or complex graph structures) are only becoming the mainstream in recent years. To address the difficulties of mining complex data, we argue that a right understanding of data characteristics (i.e., the general information of the data that is not particularly designed for any specific data mining task, but might enhance many types of data mining tasks) is important. The objective of this thesis is to study and exploit spectral information to provide quick insights into how data characteristics are beneficial to specific applications. We study issues concerning the design of how spectral information can be integrated into the needs of different types of analysis. DOCTOR OF PHILOSOPHY (SCE) 2008-09-17T09:00:58Z 2008-09-17T09:00:58Z 2007 2007 Thesis Li, W. Y. (2007). Mining high-dimensional and graph data using spectral analysis. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/2360 10.32657/10356/2360 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval Li, Wenyuan Mining high-dimensional and graph data using spectral analysis |
| title | Mining high-dimensional and graph data using spectral analysis |
| title_full | Mining high-dimensional and graph data using spectral analysis |
| title_fullStr | Mining high-dimensional and graph data using spectral analysis |
| title_full_unstemmed | Mining high-dimensional and graph data using spectral analysis |
| title_short | Mining high-dimensional and graph data using spectral analysis |
| title_sort | mining high dimensional and graph data using spectral analysis |
| topic | DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval |
| url | https://hdl.handle.net/10356/2360 |
| work_keys_str_mv | AT liwenyuan mininghighdimensionalandgraphdatausingspectralanalysis |