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An Empirical Study of Similarity Search in Stock Data
Soon, L.-K. and Lee, S.H.
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. |
Cite as: Soon, L.-K. and Lee, S.H. (2007). An Empirical Study of Similarity Search in Stock Data. In Proc. 2nd International Workshop on Integrating Artificial Intelligence and Data Mining (AIDM 2007), Gold Coast, Queensland, Australia. CRPIT, 84. Ong, K.-L., Li, W. and Gao, J., Eds. ACS. 29-36. |
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