A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering

In real-world scenarios, identifying the optimal number of clusters in a dataset is difficult task due to insufficient knowledge. Therefore, indispensability sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several assisted quantum-inspired metaheuristics have developed recent years. However, literature lacks definitive documentation state-of-the-art metaheuristic automatically datasets. This article presents brief overview process establish importance making automatic. The fundamental concepts quantum computing paradigm are also presented highlight utility algorithms. thoroughly analyses employed address various reviewed were classified according their main sources inspiration. addition, representative works each classification chosen from existing works. Thirty-six such prominent further critically analysed based on aims, used mechanisms, data specifications, merits and demerits. Comparative results performance computational time analyse As such, promises provide detailed analysis algorithms, while highlighting