Due to challenges posed by mixed data clustering, this study aims to introduce an innovative clustering-based classification algorithm that possesses the advantages of both classification and clustering techniques for mixed data analysis. The proposed algorithm employs the -prototypes algorithm with a genetic algorithm to optimize weights and centroids and utilizes the bagging method to build multiple classifiers, thereby enhancing classification performance. Furthermore, it incorporates four mutation mechanisms, including Gaussian, Cauchy, Levy, and single-point mutations, to explore optimal solutions. This study suggests using a 20% sampling ratio for the bootstrap sampling in the proposed algorithm. This ratio has been proven to be sufficient for achieving good classification performance while reducing computational time. Experimental results indicate that the proposed algorithm outperforms benchmark classifiers, demonstrating superior classification performance across five performance indicators. In addition, the loan eligibility case study offers valuable insights into applying the proposed algorithm in real-world scenarios, demonstrating that the proposed algorithm can achieve superior classification performance compared to other algorithms. It also offers managerial implications to help different industries and fields understand the appropriate timing and scenarios for implementing the algorithm.

中文翻译：

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一种混合遗传算法和 K 原型算法的集成方法，用于混合数据分类

由于混合数据聚类带来的挑战，本研究旨在引入一种创新的基于聚类的分类算法，该算法具有混合数据分析的分类和聚类技术的优点。该算法采用带有遗传算法的原型算法来优化权重和质心，并利用装袋方法构建多个分类器，从而提高分类性能。此外，它还结合了高斯、柯西、Levy和单点突变等四种突变机制来探索最优解决方案。本研究建议在所提出的算法中使用 20% 的采样率进行引导采样。该比率已被证明足以实现良好的分类性能，同时减少计算时间。实验结果表明，所提出的算法优于基准分类器，在五个性能指标上表现出优异的分类性能。此外，贷款资格案例研究为在现实场景中应用所提出的算法提供了宝贵的见解，表明所提出的算法与其他算法相比可以实现卓越的分类性能。它还提供了管理意义，帮助不同行业和领域了解实施算法的适当时机和场景。