Clustering is one of the essential branches of data mining, which has numerous practical uses in real-time applications.The Kernel K-means method (KK-means) is an extended operative clustering algorithm. However, this algorithm entirely dependent on the kernel function’s hyper-parameter. Techniques that adequately explore the search spaces are needed for real optimization problems and to get optimal answers. This paper proposes an enhanced kernel K-means clustering by employing a pigeon optimization algorithm in clustering. The suggested algorithm finds the best solution by tuning the kernel function’s hyper-parameter and alters the number of clusters simultaneously. Based on five biological and chemical datasets, the results acquired the potential result from the suggested algorithm that is compared to other approaches based on intra-cluster distances and the Rand index. Moreover, findings confirm that the suggested KK-means algorithm achieves the best computation time. The proposed algorithm achieves the necessary support for data clustering.

聚类是数据挖掘的重要分支之一，在实时应用中有着广泛的实际应用。核K均值方法(KK-means)是一种扩展的操作聚类算法。但是，该算法完全依赖于核函数的超参数。真正的优化问题和获得最佳答案需要充分探索搜索空间的技术。本文通过在聚类中采用鸽子优化算法，提出了一种增强的核K-均值聚类。建议的算法通过调整核函数​​的超参数并同时改变簇数来找到最佳解决方案。基于五个生物和化学数据集，结果从建议的算法中获得了潜在结果，该算法与基于簇内距离和兰德指数的其他方法进行了比较。此外，调查结果证实，建议的 KK-means 算法实现了最佳计算时间。所提出的算法实现了对数据聚类的必要支持。