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

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)

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Curse of dimensionality, dimension reduction, ISOMAP, KernelPCA, LLE, MVU, PCA, DBSCAN
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mr. Rahmat Widia Sembiring
Date Deposited: 24 Aug 2011 02:14
Last Modified: 22 May 2018 02:22
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