Data clustering is an important problem in computer science. The objective of data clustering is to partition data objects into some groups such that the data objects in the same group are much similar with each other while data objects in different groups are dissimilar. This paper proposes SETO-FKM method for data clustering that is a combination of stock exchange trading optimization algorithm (SETO) algorithm and fuzzy K-means (FKM). The objective of SETO is to help the FKM to escape from local optima and converge to global optimum solution. Experimental results on seven real-world data clustering benchmarks show that the SETO-FKM outperformed its counterparts.
