Application of genetic algorithm-based intuitionistic fuzzy weighted c-ordered-means algorithm to cluster analysis

With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the existing clustering algorithms are susceptible to outliers and noises as well as the initial solution. The fuzzy c-ordered-means (FCOM) method can handle outlier and noise problems by using Huber's M-estimators and Yager's OWA operator to enhance its robustness. However, the result of the FCOM algorithm is still unstable because its initial centroids are randomly generated. Besides, the attributes' weight also affect the clustering performance. Thus, this study first proposed an intuitionistic fuzzy weighted c-ordered-means (IFWCOM) algorithm that combines intuitionistic fuzzy sets (IFSs), the feature-weighted and FCOM together to improve the clustering result. Moreover, this study proposed a real-coded genetic algorithm-based IFWCOM (GA-IFWCOM) that employs the genetic algorithm to exploit the global optimal solution of the IFWCOM algorithm. Twelve benchmark datasets were used for verification in the experiment. According to the experimental results, the GA-IFWCOM algorithm achieved better clustering accuracy than the other clustering algorithms for most of the datasets.