Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction

In line with the technological developments, the current data tends to be multidimensional and high dimensional, which is more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a new representation for the data that is small...

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Main Authors:	Sembiring, Rahmat Widia, Jasni, Mohamad Zain, Abdullah, Embong
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/1203/ http://umpir.ump.edu.my/id/eprint/1203/1/ICSECS2011_Final_revision.pdf

id	ump-1203
recordtype	eprints
spelling	ump-12032018-05-22T02:22:57Z http://umpir.ump.edu.my/id/eprint/1203/ Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong QA75 Electronic computers. Computer science In line with the technological developments, the current data tends to be multidimensional and high dimensional, which is more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a new representation for the data that is smaller in volume and has the same analytical results as the original representation. To obtain an efficient processing time while clustering and mitigate curse of dimensionality, a clustering process needs data reduction. This paper proposes an alternative model for extracting multidimensional data clustering based on comparative dimension reduction. We implemented five dimension reduction techniques such as ISOMAP (Isometric Feature Mapping), KernelPCA, LLE (Local Linear Embedded), Maximum Variance Unfolded (MVU), and Principal Component Analysis (PCA). The results show that dimension reductions significantly shorten processing time and increased performance of cluster. DBSCAN within Kernel PCA and Super Vector within Kernel PCA have highest cluster performance compared with cluster without dimension reduction. 2011 Conference or Workshop Item NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1203/1/ICSECS2011_Final_revision.pdf Sembiring, Rahmat Widia and Jasni, Mohamad Zain and Abdullah, Embong (2011) Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction. In: The Second International Conference on Software Engineering and Computer System (ICSECS) 2011, June, 27-29, 2011 , Universiti Malaysia Pahang. . (Submitted)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
description	In line with the technological developments, the current data tends to be multidimensional and high dimensional, which is more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a new representation for the data that is smaller in volume and has the same analytical results as the original representation. To obtain an efficient processing time while clustering and mitigate curse of dimensionality, a clustering process needs data reduction. This paper proposes an alternative model for extracting multidimensional data clustering based on comparative dimension reduction. We implemented five dimension reduction techniques such as ISOMAP (Isometric Feature Mapping), KernelPCA, LLE (Local Linear Embedded), Maximum Variance Unfolded (MVU), and Principal Component Analysis (PCA). The results show that dimension reductions significantly shorten processing time and increased performance of cluster. DBSCAN within Kernel PCA and Super Vector within Kernel PCA have highest cluster performance compared with cluster without dimension reduction.
format	Conference or Workshop Item
author	Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong
author_facet	Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong
author_sort	Sembiring, Rahmat Widia
title	Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
title_short	Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
title_full	Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
title_fullStr	Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
title_full_unstemmed	Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
title_sort	alternative model for extracting multidimensional data based-on comparative dimension reduction
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/1203/ http://umpir.ump.edu.my/id/eprint/1203/1/ICSECS2011_Final_revision.pdf
first_indexed	2023-09-18T21:54:09Z
last_indexed	2023-09-18T21:54:09Z
_version_	1777413966551580672

Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction

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