A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION

Jian Jiang; Zhe Zhang; Huaiqing Wang; Xiaoyan Liu; Xuhao Luo; Lin Wang

A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION

Jian Jiang, Zhe Zhang, Huaiqing Wang, Xiaoyan Liu, Xuhao Luo, Lin Wang

Department of Information Systems

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

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.

Original language	English
Title of host publication	mccsis 2007 - IADIS Multi Conference on Computer Science and Information Systems
Subtitle of host publication	Proceedings of WIRELESS APPLICATIONS AND COMPUTING 2007, TELECOMUNICATIONS, NETWORKS AND SYSTEMS 2007, DATA MINING 2007
Editors	Jörg Roth, Jairo Gutiérrez, Ajith P. Abraham
Publisher	IADIS Press
Pages	198-202
ISBN (Print)	9789728924409
Publication status	Published - Jul 2007
Event	IADIS Multi Conference on Computer Science and Information Systems (MCCSIS 2007) - Lisbon, Portugal Duration: 3 Jul 2007 → 8 Jul 2007 http://www.iadisportal.org/previouseditions/MCCSIS_2007.pdf

Publication series

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

Conference

Conference	IADIS Multi Conference on Computer Science and Information Systems (MCCSIS 2007)
Place	Portugal
City	Lisbon
Period	3/07/07 → 8/07/07
Internet address	http://www.iadisportal.org/previouseditions/MCCSIS_2007.pdf

Research Keywords

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

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Jiang, J., Zhang, Z., Wang, H., Liu, X., Luo, X., & Wang, L. (2007). A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION. In J. Roth, J. Gutiérrez, & A. P. Abraham (Eds.), 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 (pp. 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 ). IADIS Press.

Jiang, Jian ; Zhang, Zhe ; Wang, Huaiqing et al. / A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION. 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. editor / Jörg Roth ; Jairo Gutiérrez ; Ajith P. Abraham. IADIS Press, 2007. pp. 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 ).

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title = "A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION",

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.",

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author = "Jian Jiang and Zhe Zhang and Huaiqing Wang and Xiaoyan Liu and Xuhao Luo and Lin Wang",

year = "2007",

month = jul,

language = "English",

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booktitle = "mccsis 2007 - IADIS Multi Conference on Computer Science and Information Systems",

note = "IADIS Multi Conference on Computer Science and Information Systems (MCCSIS 2007) ; Conference date: 03-07-2007 Through 08-07-2007",

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Jiang, J, Zhang, Z, Wang, H, Liu, X, Luo, X & Wang, L 2007, A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION. in J Roth, J Gutiérrez & AP Abraham (eds), 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. MCCSIS - IADIS Multi Conference on Computer Science and Information Systems - Proceedings of Wireless Applications and Computing, Telecommunications, Networks and Systems and Data Mining , IADIS Press, pp. 198-202, IADIS Multi Conference on Computer Science and Information Systems (MCCSIS 2007), Lisbon, Portugal, 3/07/07.

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

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AU - Luo, Xuhao

AU - Wang, Lin

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AB - 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.

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Jiang J, Zhang Z, Wang H, Liu X, Luo X, Wang L. A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION. In Roth J, Gutiérrez J, Abraham AP, editors, 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. 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 ).

A NEW APPROACH TO IMPROVE MULTI-DIMENSIONAL STOCK DATA REDUCTION

Abstract

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Research Keywords

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