Genetic Algorithm Based Fuzzy c-Ordered-Means to Cluster Analysis

Clustering is an important technique which is used to discover the data structure. Clustering is applied in many areas, such as customer segmentation, image recognition, social science, and so on. However, most of the existing clustering methods suffer from two major drawbacks including 1) the susceptibility of clustering result due to the randomly initial centers and 2) the sensitivity of outliers and noise data. To solve these two problems, this study proposes a new algorithm named genetic algorithm-based fuzzy c-ordered-means algorithm (GA-FCOM). Herein, the fuzzy c-ordered-means algorithm (FCOM) can deal with noise and outliers data while the genetic algorithm is employed to obtain the optimal initial centroids efficiently during the clustering process. An experiment is conducted using the benchmark datasets collected from the UCI machine repository to validate the proposed algorithm. The computational results indicate that the proposed GA-FCOM outperforms fuzzy c-means algorithm (FCM) and FCOM in terms of both accuracy and objective function values.