An application of sine cosine algorithm-based fuzzy possibilistic c-ordered means algorithm to cluster analysis

Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognition, social science, and so on. However, in real-world applications, there are a lot of noises or outliers which will seriously influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. To overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means and fuzzy c-ordered means to propose a fuzzy possibilistic c-ordered means (FPCOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (SCA) combined with FPCOM to improve the clustering results. The proposed algorithm is named SCA-FPCOM algorithm. Ten benchmark datasets collected from the UCI machine repository were used to validate the proposed algorithm in terms of adjusted rand index and the Silhouette coefficient. According to the experimental results, the SCA-FPCOM algorithm can obtain better results than other algorithms.

Automated summary

This study proposes a new clustering algorithm SCA-FPCOM, which combines the advantages of probability c-means and fuzzy c-ordered means, and utilizes a sine cosine algorithm to improve clustering results. Experimental results demonstrate that the SCA-FPCOM algorithm performs well and achieves better results than other algorithms.