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Density-based clustering algorithms, such density peaks clustering (DPC), have the ability to identify clusters of any shape, automatically detect and exclude abnormal points, and accurately determine the number of clusters. Nevertheless, the sample distribution process is susceptible to incidental errors, and the density peaks clustering approach is ineffective at grouping data with fluctuating densities. This research presents the density peaks clustering method, which combines the inverse neighbors and k-nearest neighbors’ ideas. The algorithm devises a cluster weight formula to determine the optimal weights for the samples in order to complete the final clustering. It categorizes the samples into non-boundary and boundary points by analyzing the characteristics of the inverse nearest neighbor. Additionally, it incorporates the concepts of k-nearest neighbor and inverse nearest neighbor to calculate the local density of the samples and identify the highest density point. Ultimately, the method is assessed by comparing it to other standard methods using both synthetic datasets with complex structures and real datasets. The results showcased the efficacy of our approach in effectively mitigating the "domino effect" and accurately selecting sample density maxima in sparsely populated regions.
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