Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Exact indexing of dynamic time warping
Knowledge and Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A decade of progress in indexing and mining large time series databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Explorative Data Mining on Stock Data --- Experimental Results and Findings
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Comparative analysis of data mining techniques for financial data using parallel processing
Proceedings of the 7th International Conference on Frontiers of Information Technology
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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.