An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data
Data mining techniques are used in various industries, including database marketing, web analysis, information retrieval and bioinformatics to gain a better knowledge extraction. However, if data mining techniques are applied on real datasets, a problem that often comes up is that missing values occ...
| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | 英語 英語 英語 |
| 出版事項: |
2017
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| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/7817/ |
| Abstract | Abstract here |
| _version_ | 1855520734438227968 |
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| author | Samat, Nurul Ashikin |
| author_facet | Samat, Nurul Ashikin |
| author_sort | Samat, Nurul Ashikin |
| description | Data mining techniques are used in various industries, including database marketing, web analysis, information retrieval and bioinformatics to gain a better knowledge extraction. However, if data mining techniques are applied on real datasets, a problem that often comes up is that missing values occur in the datasets. Since the missing values may confuse the data mining process and causing the knowledge extracted unreliable, there is a need to handle the missing values. Therefore, researchers ar.e coming out with imputation methods in the preproce_ssing |
| format | Thesis |
| id | uthm-7817 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English English |
| publishDate | 2017 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | uthm-78172022-10-12T02:21:28Z http://eprints.uthm.edu.my/7817/ An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data Samat, Nurul Ashikin QA Mathematics QA71-90 Instruments and machines Data mining techniques are used in various industries, including database marketing, web analysis, information retrieval and bioinformatics to gain a better knowledge extraction. However, if data mining techniques are applied on real datasets, a problem that often comes up is that missing values occur in the datasets. Since the missing values may confuse the data mining process and causing the knowledge extracted unreliable, there is a need to handle the missing values. Therefore, researchers ar.e coming out with imputation methods in the preproce_ssing 2017-08 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/7817/2/24p%20NURUL%20ASHIKIN%20SAMAT.pdf text en http://eprints.uthm.edu.my/7817/1/NURUL%20ASHIKIN%20SAMAT%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/7817/3/NURUL%20ASHIKIN%20SAMAT%20WATERMARK.pdf Samat, Nurul Ashikin (2017) An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | QA Mathematics QA71-90 Instruments and machines Samat, Nurul Ashikin An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data |
| thesis_level | Master |
| title | An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data |
| title_full | An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data |
| title_fullStr | An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data |
| title_full_unstemmed | An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data |
| title_short | An improved imputation method based on fuzzy c-means and particle swarm optimization for missing data |
| title_sort | improved imputation method based on fuzzy c means and particle swarm optimization for missing data |
| topic | QA Mathematics QA71-90 Instruments and machines |
| url | http://eprints.uthm.edu.my/7817/ |
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