Plant-derived natural compounds are significant resources for the discovery of potential anticancer drugs. While research in the plant-based anticancer field has surged in recent years, systematic bibliometric analyses covering a longer period and containing up-to-date publications remain scarce. Here, we conducted a bibliometric analysis of literature on the anticancer properties of plant natural compounds over the past three decades, leveraging the bibliometric framework and open-access platform, KNIME. Our findings showed that the number of plant anticancer-related publications underwent an accelerating growth from 1992 to 2023. The country and institution analyses revealed that countries with traditional medical systems contributed a large portion of publications in the plant anticancer field, such as India, China, and South Korea. This study also highlighted the top ten eminent researchers and publications, assisting researchers in identifying pivotal literature. The primary publications were domains of chemistry and biology-related fields, such as Pharmacology & Pharmacy, Plant Sciences, and Biochemistry & Molecular Biology. Additionally, we noted that flavonoids have been focal plant compounds in anticancer, with strong anticancer potential. Our study provides new insights into the progress and trends in the plant anticancer field and will assist researchers in grasping the future research direction.

Research on the fairness of spectral clustering has gradually increased attention. Normally, existing methods of fair spectral clustering add a fairness constraint to the original objective function so that fairness is guaranteed. However, similar to the solver of traditional spectral clustering, that of fairness spectral clustering has to relax a discrete value condition into an arbitrary one, which leads to the deterioration of both fairness and clustering quality. Moreover, the eigen-problem is inevitable in the solver, which takes $\mathcal{O}\left(n^3\right)$ time complexity and is not available for large-scale data. In this paper, we propose a fair spectral clustering algorithm by employing the coordinate descent method to find the solution. As the relaxation of the discreteness condition is discarded, the fairness is improved. Furthermore, we refine the process of coordinate descent by avoiding redundant calculations, and as a result, the time complexity is reduced from $\mathcal{O}\left(n^3\right)$ to $\mathcal{O}\left(n^2\right)$. Additionally, the importance of clustering quality and fairness is symmetric; hence, we achieve a trade-off between them by adjusting the parameters. The experimental findings, obtained from both real-world and synthetic datasets, clearly illustrate that our proposal delivers superior fairness and clustering quality with the best BAL compared to other fair clustering methods. In addition, our method is more efficient than existing fair spectral clustering algorithms.