An empirical study of similarity search in stock data


Soon, Lay-Ki and Sang, Ho Lee (2007) An empirical study of similarity search in stock data. In: Proceeding AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining. Australian Computer Society, pp. 31-38. ISBN 978-1-920682-65-1

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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.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 21 Nov 2013 04:37
Last Modified: 21 Nov 2013 04:37


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