A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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Author(s)

  • Jian Jiang
  • Zhe Zhang
  • Huaiqing Wang
  • Xiaoyan Liu
  • Xuhao Luo
  • And 1 others
  • Lin Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationmccsis 2007 - IADIS Multi Conference on Computer Science and Information Systems
Subtitle of host publicationProceedings of WIRELESS APPLICATIONS AND COMPUTING 2007, TELECOMUNICATIONS, NETWORKS AND SYSTEMS 2007, DATA MINING 2007
EditorsJörg Roth, Jairo Gutiérrez, Ajith P. Abraham
PublisherIADIS Press
Pages198-202
ISBN (print)9789728924409
Publication statusPublished - Jul 2007

Publication series

NameMCCSIS - IADIS Multi Conference on Computer Science and Information Systems - Proceedings of Wireless Applications and Computing, Telecommunications, Networks and Systems and Data Mining

Conference

TitleIADIS Multi Conference on Computer Science and Information Systems (MCCSIS 2007)
PlacePortugal
CityLisbon
Period3 - 8 July 2007

Link(s)

Check@CityULib
Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-85090734881&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(7bef9f35-fcb0-4a3d-9b6d-debd48d50cb9).html

Abstract

With the increase of economic globalization and evolution of information technology, high-dimensional stock data reduction has become an essential part as pre-processing technique for data compression and effective future data mining process. In this paper, we study the effect of dimension reduction technique, which is commonly used for correlated multi-dimensional data. We use PCA as one of the representatives of the reduction techniques. And we improve the results of Principle Component Analysis (PCA) by using proper pre-processing approach based on Perceptually Important Point (PIP) algorithm. By using our approach, we can improve the efficiency of dimension reduction to stock data. Encouraging experiment is reported from the tests that our approach can provide a much higher reservation ratio for the reduced multi-dimensional stock data.

Research Area(s)

  • Data mining, Dimension reduction, Multi-dimensional stock data, Perceptually Important Algorithm (PIP), Pre-processing, Principal component analysis (PCA)

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Publication fingerprints text

100
Stock Data Keyphrases
100
Data Reduction Keyphrases
100
Component Analysis Engineering
100
Dimension Reduction Engineering
100
reduction INIS
100
stocks INIS
100
data INIS
100
Principle Component... Computer Science
100
Procedure Computer Science
100
Data Processing Computer Science
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Citation Format(s)

[ RIS ] [ BibTeX ]
A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION. / Jiang, Jian; Zhang, Zhe; Wang, Huaiqing et al.
mccsis 2007 - IADIS Multi Conference on Computer Science and Information Systems: Proceedings of WIRELESS APPLICATIONS AND COMPUTING 2007, TELECOMUNICATIONS, NETWORKS AND SYSTEMS 2007, DATA MINING 2007. ed. / Jörg Roth; Jairo Gutiérrez; Ajith P. Abraham. IADIS Press, 2007. p. 198-202 (MCCSIS - IADIS Multi Conference on Computer Science and Information Systems - Proceedings of Wireless Applications and Computing, Telecommunications, Networks and Systems and Data Mining ).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review