An empirical study of similarity search in stock data

Lay Ki Soon; Sang Ho Lee

An empirical study of similarity search in stock data

Lay Ki Soon, Sang Ho Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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.

Original language	English
Title of host publication	Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007
Editors	Kok-Leong Ong, Junbin Gao, Wenyuan Li
Publisher	Australian Computer Science Society
ISBN (Print)	9781920682651
Publication status	Published - 2007
Externally published	Yes
Event	International Workshop on Integrating AI and Data Mining 2007 - Gold Coast, Australia Duration: 1 Dec 2007 → 1 Dec 2007 Conference number: 2nd

Publication series

Name	Conferences in Research and Practice in Information Technology Series
Volume	84
ISSN (Print)	1445-1336

Workshop

Workshop	International Workshop on Integrating AI and Data Mining 2007
Abbreviated title	AIDM 2007
Country/Territory	Australia
City	Gold Coast
Period	1/12/07 → 1/12/07

Keywords

Computational finance
Data normalization
Financial data mining
Similarity search

Cite this

Soon, L. K., & Lee, S. H. (2007). An empirical study of similarity search in stock data. In K-L. Ong, J. Gao, & W. Li (Eds.), Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007 (Conferences in Research and Practice in Information Technology Series; Vol. 84). Australian Computer Science Society.

Soon, Lay Ki ; Lee, Sang Ho. / An empirical study of similarity search in stock data. Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007. editor / Kok-Leong Ong ; Junbin Gao ; Wenyuan Li. Australian Computer Science Society, 2007. (Conferences in Research and Practice in Information Technology Series).

@inproceedings{f8feafb4d39548f09fbe4a071afa1a58,

title = "An empirical study of similarity search in stock data",

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.",

keywords = "Computational finance, Data normalization, Financial data mining, Similarity search",

author = "Soon, {Lay Ki} and Lee, {Sang Ho}",

note = "Publisher Copyright: {\textcopyright} 2007, Australian Computer Society, Inc. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; International Workshop on Integrating AI and Data Mining 2007, AIDM 2007 ; Conference date: 01-12-2007 Through 01-12-2007",

year = "2007",

language = "English",

isbn = "9781920682651",

series = "Conferences in Research and Practice in Information Technology Series",

publisher = "Australian Computer Science Society",

editor = "Kok-Leong Ong and Junbin Gao and Wenyuan Li",

booktitle = "Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007",

}

Soon, LK & Lee, SH 2007, An empirical study of similarity search in stock data. in K-L Ong, J Gao & W Li (eds), Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007. Conferences in Research and Practice in Information Technology Series, vol. 84, Australian Computer Science Society, International Workshop on Integrating AI and Data Mining 2007, Gold Coast, Queensland, Australia, 1/12/07.

An empirical study of similarity search in stock data. / Soon, Lay Ki; Lee, Sang Ho.

Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007. ed. / Kok-Leong Ong; Junbin Gao; Wenyuan Li. Australian Computer Science Society, 2007. (Conferences in Research and Practice in Information Technology Series; Vol. 84).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - An empirical study of similarity search in stock data

AU - Soon, Lay Ki

AU - Lee, Sang Ho

N1 - Conference code: 2nd

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Computational finance

KW - Data normalization

KW - Financial data mining

KW - Similarity search

UR - http://www.scopus.com/inward/record.url?scp=84860376679&partnerID=8YFLogxK

M3 - Conference Paper

AN - SCOPUS:84860376679

SN - 9781920682651

T3 - Conferences in Research and Practice in Information Technology Series

BT - Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007

A2 - Ong, Kok-Leong

A2 - Gao, Junbin

A2 - Li, Wenyuan

PB - Australian Computer Science Society

T2 - International Workshop on Integrating AI and Data Mining 2007

Y2 - 1 December 2007 through 1 December 2007

ER -

Soon LK, Lee SH. An empirical study of similarity search in stock data. In Ong K-L, Gao J, Li W, editors, Integrating Artificial Intelligence and Data Mining - Proceeding of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, AIDM 2007. Australian Computer Science Society. 2007. (Conferences in Research and Practice in Information Technology Series).

An empirical study of similarity search in stock data

Abstract

Publication series

Workshop

Keywords

Other files and links

Cite this