Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
393157 | Information Sciences | 2013 | 11 Pages |
Abstract
Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ibrahim Berkan Aydilek, Ahmet Arslan,