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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