An new algorithm-based rough set for selecting clustering attribute in categorical data
Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally compl...
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my.utm.924642021-09-30T15:11:47Z http://eprints.utm.my/id/eprint/92464/ An new algorithm-based rough set for selecting clustering attribute in categorical data Baroud, Muftah Mohamed Jomah Mohd. Hashim, Siti Zaiton Zainal, Anazida Ahnad, Jamilah QA75 Electronic computers. Computer science Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally complexity and low purity. In this study, the researcher looked at the limitations of the two rough set based techniques used, Information-Theoretic Dependency Roughness (ITDR) and Maximum Indiscernible Attribute (MIA). They also proposed a novel method for selecting clustering attributes, Maximum mean Attribute (MMA). They compared the performance of MMA, ITDR and MIA technique, using UCI and benchmark datasets. Their results validated the performance of the MMA with regards to its purity and computational complexity. 2020 Conference or Workshop Item PeerReviewed Baroud, Muftah Mohamed Jomah and Mohd. Hashim, Siti Zaiton and Zainal, Anazida and Ahnad, Jamilah (2020) An new algorithm-based rough set for selecting clustering attribute in categorical data. In: 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, 6 - 7 March 2020, Coimbatore, India. http://dx.doi.org/10.1109/ICACCS48705.2020.907448313581364 |
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QA75 Electronic computers. Computer science Baroud, Muftah Mohamed Jomah Mohd. Hashim, Siti Zaiton Zainal, Anazida Ahnad, Jamilah An new algorithm-based rough set for selecting clustering attribute in categorical data |
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Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally complexity and low purity. In this study, the researcher looked at the limitations of the two rough set based techniques used, Information-Theoretic Dependency Roughness (ITDR) and Maximum Indiscernible Attribute (MIA). They also proposed a novel method for selecting clustering attributes, Maximum mean Attribute (MMA). They compared the performance of MMA, ITDR and MIA technique, using UCI and benchmark datasets. Their results validated the performance of the MMA with regards to its purity and computational complexity. |
format |
Conference or Workshop Item |
author |
Baroud, Muftah Mohamed Jomah Mohd. Hashim, Siti Zaiton Zainal, Anazida Ahnad, Jamilah |
author_facet |
Baroud, Muftah Mohamed Jomah Mohd. Hashim, Siti Zaiton Zainal, Anazida Ahnad, Jamilah |
author_sort |
Baroud, Muftah Mohamed Jomah |
title |
An new algorithm-based rough set for selecting clustering attribute in categorical data |
title_short |
An new algorithm-based rough set for selecting clustering attribute in categorical data |
title_full |
An new algorithm-based rough set for selecting clustering attribute in categorical data |
title_fullStr |
An new algorithm-based rough set for selecting clustering attribute in categorical data |
title_full_unstemmed |
An new algorithm-based rough set for selecting clustering attribute in categorical data |
title_sort |
new algorithm-based rough set for selecting clustering attribute in categorical data |
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2020 |
url |
http://eprints.utm.my/id/eprint/92464/ http://dx.doi.org/10.1109/ICACCS48705.2020.907448313581364 |
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1713199735505944576 |
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13.159267 |