Rohani, Abu Bakar and Watada, Junzo (2011) Empirical robustness evaluation of DNA-based clustering methods. International Journal of Intelligent Computing in Medical Sciences and Image Processing, 4 (1). pp. 1-12. ISSN 1931-308X. (Published)
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Abstract
DNA-based computation is one of the latest computation paradigms. Compared to conventional methods that obtain their end results via electronic processes, aDNA-based approach obtains its result from bio-chemical reactions. It is essential in this approach for all experimental processes to be performed without fault. However, some errors may occur while carrying out these bio-chemical experiments. Consequently, it is necessary to overcome their weaknesses. The aim of this study is to examine the robustness of DNA-based techniques in solving a clustering problem. In the broadest sense, robustness can be defined as being able to withstand stresses, pressures, or changes in procedure or circumstance. To examine the robustness of the approach, this research examined the impact of error or added noise on DNA-based procedure results. Comparative studies of different error sets are also provided here. Additionally, two well-known conventional clustering algorithms (Fuzzy C-means and k-means) were applied to the same error sets, to study the reliability and validity of results when comparing DNA-based clustering.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | DNA-based computation; Clustering; DNA-based clustering; Fuzzy C-Means; K-means; Clustering robustness |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 11 Feb 2020 06:58 |
| Last Modified: | 11 Feb 2020 06:58 |
| URI: | http://umpir.ump.edu.my/id/eprint/25578 |
| Download Statistic: | View Download Statistics |
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