Data clustering with grasshopper optimization algorithm

Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the possibility of using novel nature-inspired technique — Grasshopper Optimization Algorithm (GOA) — to generate accurate data clusterings. As a quality measure of produced solutions internal clustering validation measure of Calinski-Harabasz index is being employed. This paper provides description of proposed algorithm along with its experimental evaluation for a set of benchmark instances. Over a course of our study it was established that clustering based on GOA is characterized by high accuracy — when compared with standard K-means procedure.