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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/1203/1/ICSECS2011_Final_revision.pdf |
| _version_ | 1825821084741533696 |
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| author | Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong |
| author_facet | Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong |
| author_sort | Sembiring, Rahmat Widia |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T11:36:13Z |
| format | Conference or Workshop Item |
| id | UMPir1203 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:36:13Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | UMPir12032018-05-22T02:22:57Z 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) (Submitted) |
| 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 |
| title | 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_short | Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction |
| title_sort | alternative model for extracting multidimensional data based on comparative dimension reduction |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/1203/1/ICSECS2011_Final_revision.pdf |
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