An empirical study of similarity search in stock data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Soon, L.-K. | - |
dc.contributor.author | Lee, S.H. | - |
dc.date.available | 2019-04-10T11:21:16Z | - |
dc.date.created | 2018-04-17 | - |
dc.date.issued | 2007 | - |
dc.identifier.isbn | 9781920682651 | - |
dc.identifier.issn | 1445-1336 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/33690 | - |
dc.description.abstract | 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. © 2007, Australian Computer Society, Inc. | - |
dc.publisher | Australian Computer Society | - |
dc.relation.isPartOf | Conferences in Research and Practice in Information Technology Series | - |
dc.title | An empirical study of similarity search in stock data | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007, v.84 | - |
dc.description.journalClass | 2 | - |
dc.identifier.scopusid | 2-s2.0-84860376679 | - |
dc.citation.title | 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007 | - |
dc.citation.volume | 84 | - |
dc.contributor.affiliatedAuthor | Lee, S.H. | - |
dc.type.docType | Conference Paper | - |
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