Detecting Effectiveness of Outliers and Noisy Data on Fuzzy System Using FCM
Fuzzy systems which are an artificial intelligent technique are applicable for controlling and decision support systems. Fuzzy systems are created using Membership Functions (MFs) which modeled based on dataset. Therefore, there is relation between uncertainty of input data and fuzziness expressed by MFs. Outliers and noisy data are kinds of uncertainty which affect on membership function. Thus, MFs will not be a robustness model to make an accurate decision for controlling and decision making. However, the isolation of outliers is important both for improving the quality of original data and for reducing the impact of outlying value in processes of knowledge discovery and MFs.