Co-clustering aims to cluster the rows and columns of data simultaneously and can be often formulated as a two-objective optimization problem (one objective for rows and the other for columns) and the solution is a Pareto-optimal solution set in principle. Existing methods usually convert the co-clustering problem into a single-objective optimization problem by setting a hyper-parameter between the two objectives. However, finding a good value of is not easy because of its large parameter space; also, there may exist many equally good s. In this paper, a nondominated sorting genetic model (NSGC) is proposed to tackle the co-clustering problem, totally bypassing the trade-off parameter and returning to the original two-objective problem. The core of our model is to group a row objective function and a column objective function and integrate them into a genetic algorithm as the fitness functions. After this reformulation, we follow a standard genetic algorithm procedure to iteratively find the Pareto-optimal solutions. Finally, to fish out a single best solution we further design a sorting criterion according to which the Pareto-optimal solution set can be totally ordered. Extensive experiments with 16 public datasets are conducted, and the results demonstrate the superiority of our approach.

中文翻译：

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共聚类的非支配排序遗传模型

联合聚类旨在同时对数据的行和列进行聚类，并且通常可以表述为双目标优化问题（一个目标针对行，另一个目标针对列），并且解决方案原则上是帕累托最优解集。现有方法通常通过在两个目标之间设置超参数将共聚类问题转化为单目标优化问题。然而，由于参数空间很大，找到一个好的值并不容易；而且，可能存在许多同样好的 s。本文提出一种非支配排序遗传模型（NSGC）来解决共聚类问题，完全绕过权衡参数，回到原来的双目标问题。我们模型的核心是将行目标函数和列目标函数分组，并将它们集成到遗传算法中作为适应度函数。重新制定后，我们遵循标准遗传算法程序来迭代找到帕累托最优解。最后，为了找出单个最佳解决方案，我们进一步设计了一个排序标准，根据该标准可以对帕累托最优解集进行完全排序。对 16 个公共数据集进行了广泛的实验，结果证明了我们方法的优越性。