An empirical study of similarity search in stock data

Lay Ki Soon, Sang Ho Lee

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIntegrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007
EditorsKok-Leong Ong, Junbin Gao, Wenyuan Li
PublisherAustralian Computer Science Society
ISBN (Print)9781920682651
Publication statusPublished - 2007
Externally publishedYes
EventInternational Workshop on Integrating AI and Data Mining 2007 - Gold Coast, Australia
Duration: 1 Dec 20071 Dec 2007
Conference number: 2nd

Publication series

NameConferences in Research and Practice in Information Technology Series
ISSN (Print)1445-1336


WorkshopInternational Workshop on Integrating AI and Data Mining 2007
Abbreviated titleAIDM 2007
CityGold Coast


  • Computational finance
  • Data normalization
  • Financial data mining
  • Similarity search

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