Abstract
Using certain artificial intelligence techniques, stock data mining has given encouraging results in both trend analysis and similarity search. However, representing stock data effectively is a key issue in ensuring the success of a data mining process. In this paper, we aim to compare the performance of numeric and symbolic data representation of a stock dataset in terms of similarity search. Given the properly normalized dataset, our empirical study suggests that the results produced by numeric stock data are more consistent as compared to symbolic stock data.
Original language | English |
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Title of host publication | Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007 |
Editors | Kok-Leong Ong, Junbin Gao, Wenyuan Li |
Publisher | Australian Computer Science Society |
ISBN (Print) | 9781920682651 |
Publication status | Published - 2007 |
Externally published | Yes |
Event | International Workshop on Integrating AI and Data Mining 2007 - Gold Coast, Australia Duration: 1 Dec 2007 → 1 Dec 2007 Conference number: 2nd |
Publication series
Name | Conferences in Research and Practice in Information Technology Series |
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Volume | 84 |
ISSN (Print) | 1445-1336 |
Workshop
Workshop | International Workshop on Integrating AI and Data Mining 2007 |
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Abbreviated title | AIDM 2007 |
Country/Territory | Australia |
City | Gold Coast |
Period | 1/12/07 → 1/12/07 |
Keywords
- Computational finance
- Data normalization
- Financial data mining
- Similarity search