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Density peak clustering (DPC) has shown promising results for many complex problems when compared with other existing clustering techniques. Inspite of many advantages, DPC suffers with lack of cluster centroids and cut-off distance identification. Cut-off distance is the prominent parameter used in the calculation of local density. The improper choice of cut-off distance leads to improper cluster results. Currently, the cut-off distance is selected using decision graph or delta density or knee point detection or silhouette score or kernel functions. The main problem with the above functions for selecting the cut-off distance in DPC is that they often rely on heuristic or visually subjective criteria, making the choice of the optimal cut-off distance challenging and potentially sensitive to data characteristics. By leveraging metaheuristic optimisation algorithms, the process of selecting the cut-off distance becomes less subjective and data-driven, potentially leading to improved clustering results in DPC. This motivated us to work on the choice of cut-off distance by the usage of remora optimisation algorithm (ROA). The cluster results are improved by the usage of remora in selection of reliable cut-off distance (. The effectiveness of the updated DPC with ROA is evaluated by applying on the eight datasets and compared with K-means, traditional DPC, DPC merged with other optimisation results. The three parameters used here to check the quality of the cluster are homogeneity, completeness and silhouette analysis. ROA is new and built on the inspiration of remora which moves from one place to another using the sea fishes like shark, whale, sword fish, etc. It is clear from the results that DPC with ROA has produced the better homogeneity value of 0.807, completeness of 0.699 and silhouette analysis of 0.79 than the other clustering algorithms.

Nature inspired-based remora optimisation algorithm for enhancement of density peak clustering