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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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
2010
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| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/39957 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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