A review of quantum-inspired metaheuristic algorithms for automatic clustering

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clus tering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algo rithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits.Web of Science119art. no. 201

Automatic Graph-Based clustering for security logs

Computer security events are recorded in several log files. It is necessary to cluster these logs to discover security threats, detect anomalies, or identify a particular error. A problem arises when large quantities of security log data need to be checked as existing tools do not provide sufficiently sophisticated grouping results. In addition, existing methods need user input parameters and it is not trivial to find optimal values for these. Therefore, we propose a method for the automatic clustering of security logs. First, we present a new graph-theoretic approach for security log clustering based on maximal clique percolation. Second, we add an intensity threshold to the obtained maximal clique to consider the edge weight before proceeds to the percolations. Third, we use the simulated annealing algorithm to optimize the number of percolations and intensity threshold for maximal clique percolation. The entire process is automatic and does not need any user input. Experimental results on various real-world datasets show that the proposed method achieves superior clustering results compared to other methods