An Empirical Study of Similarity Search in Stock Data
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Financial data are conventionally represented in numeric format for data mining purpose. However, recent works have demonstrated promising results of representing financial data symbolically. For an instance, Kovalerchuk et al. (2002) argues that symbolic relational data mining is more suitable in incorporating background knowledge. Their proposed methodology outperforms numeric financial data in generating IF-Then rules. In (Ting et al. 2006), sequential and non-sequential association rule mining (ARM) were used to perform intra and inter-stock pattern mining, where each stock is represented symbolically based on its performance with respect to a user-defined threshold. Similarly, we...
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An Empirical Study of Similarity Search in Stock Data
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An Empirical Study of Similarity Search in Stock Data
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