Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points. However, the local density of DPC strongly depends on the cutoff distance which must be prespecified; in addition, the strategy assigns each remaining point to the same cluster as its nearest neighbor of higher density in descending order of local density, which is likely to cause cluster label error propagation. To overcome these limitations, we propose an improved DPC by introducing weighted local density sequence and two-stage assignment strategies, called DPCSA. Many previous improved DPC algorithms neglect additional complexity, whereas DPCSA incorporates the nearest neighbor dynamic table to enhance clustering efficiency. The experimental results for 12 artificial and 11 real-world datasets, including Olivetti face, verify that the DPCSA clustering performance is significantly superior to DPC and DPC via heat diffusion (HDDPC), and slightly superior to fuzzy weighted k-nearest neighbors density peak clustering (FKNNDPC). In addition, the DPCSA is more computationally efficient than FKNNDPC and HDDPC, but less than DPC. The source code of DPCSA is available at https://github.com/Yu123456/DPCSA. INDEX TERMS Cluster analysis, density peaks, K-nearest neighbors, local density, nearest neighbor dynamic table.

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Purpose – The purpose of this paper is to present systematic optimal design procedures for the Gough-Stewart platforms used as engineering motion simulators. Design/methodology/approach – Three systematic optimal design procedures are proposed to solve the engineering design problems for the Gough-Stewart platform used as motion simulators. In these systematic optimal design procedures, two contradicting design optimality criteria with good representations of performances of the Gough-Stewart platforms are chosen as the objective functions. In addition, the two objective function optimization problems are solved by using the multi-objective evolutionary algorithms. Findings – In the systematic optimal design procedures, multiple compromised design solutions are found by using Elitist Non-Dominated Sorting Genetic Algorithm version II in the primary design stage, and many candidates can be used in the secondary design stage for higher decisions. Two higher decision methods have been presented to choose the final solutions. Originality/value – This paper proposes three systematic optimal design procedures to solve the practical design problems of the Gough-Stewart platforms used as motion simulators, which are very important for the engineering designers.

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