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...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English English |
| Published: |
2017
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7817/ http://eprints.uthm.edu.my/7817/2/24p%20NURUL%20ASHIKIN%20SAMAT.pdf http://eprints.uthm.edu.my/7817/1/NURUL%20ASHIKIN%20SAMAT%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7817/3/NURUL%20ASHIKIN%20SAMAT%20WATERMARK.pdf |
| Summary: | 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 |
|---|