Quasi subgraphs, noise tolerance, and financial market applications
This report first introduces some of the background information related to value investment, data mining and graph theories. An implemented application used for the project is called Complete QB Miner which co – clusters stocks and financial ratios. For the data pre – processing/data mining process,...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10356/39957 |
| _version_ | 1826109645367803904 |
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| author | Li, Yi Wen. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Li, Yi Wen. |
| author_sort | Li, Yi Wen. |
| collection | NTU |
| description | This report first introduces some of the background information related to value investment, data mining and graph theories. An implemented application used for the project is called Complete QB Miner which co – clusters stocks and financial ratios. For the data pre – processing/data mining process, an open source data mining tool called WEKA is studied and used. In particular, different data discretization techniques which supported by WEKA are separately applied on the data and the results are discussed. This report also provides some coverage on the data mining technologies that have been used during the whole project. |
| first_indexed | 2024-10-01T02:21:18Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/39957 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:21:18Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/399572023-03-03T20:42:22Z Quasi subgraphs, noise tolerance, and financial market applications Li, Yi Wen. School of Computer Engineering Zhang Jie DRNTU::Engineering::Computer science and engineering::Information systems::Database management This report first introduces some of the background information related to value investment, data mining and graph theories. An implemented application used for the project is called Complete QB Miner which co – clusters stocks and financial ratios. For the data pre – processing/data mining process, an open source data mining tool called WEKA is studied and used. In particular, different data discretization techniques which supported by WEKA are separately applied on the data and the results are discussed. This report also provides some coverage on the data mining technologies that have been used during the whole project. Bachelor of Engineering (Computer Science) 2010-06-08T06:26:08Z 2010-06-08T06:26:08Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39957 en Nanyang Technological University 40 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Information systems::Database management Li, Yi Wen. Quasi subgraphs, noise tolerance, and financial market applications |
| title | Quasi subgraphs, noise tolerance, and financial market applications |
| title_full | Quasi subgraphs, noise tolerance, and financial market applications |
| title_fullStr | Quasi subgraphs, noise tolerance, and financial market applications |
| title_full_unstemmed | Quasi subgraphs, noise tolerance, and financial market applications |
| title_short | Quasi subgraphs, noise tolerance, and financial market applications |
| title_sort | quasi subgraphs noise tolerance and financial market applications |
| topic | DRNTU::Engineering::Computer science and engineering::Information systems::Database management |
| url | http://hdl.handle.net/10356/39957 |
| work_keys_str_mv | AT liyiwen quasisubgraphsnoisetoleranceandfinancialmarketapplications |