This paper presents an Artificial Bee Colony (ABC) optimization based algorithm for co-clustering of high-dimensional data. The ABC algorithm is used for optimization problems including data clustering. We incorporate aspects of co-clustering by embedding it into the objective function used for clustering by the ABC algorithm. Instead of a linear metric, such as the Euclidean distance, we propose the use of higher order correlations to build similarity between rows and columns, each based on the other. This measure uses co-evolving similarities which when embedded into the objective function results in optimizing the co-clusters. The search space is also explored in the vicinity of the solutions produced by the ABC algorithm using three local search methods – the first is a heuristic based on computing the cluster means; the second uses the analytical gradient of the objective with respect to a centroid to find lower cost solutions in the vicinity; and, the third is a hybrid of the first two methods. Numerical experiments show significant improvement in the search for optimal clustering by incorporating new similarity metric and optimized local search method. Finally, the algorithm is shown to be highly scalable for parallel architectures for both distributed and shared memory systems. Theoretically, the best iso-efficiency function of $\Theta$ (p log p) for fully connected network with p processors is also computed for the parallel algorithm.

本文提出了一种基于人工蜂群（ABC）优化的算法来对高维数据进行共同聚类。ABC算法用于解决优化问题，包括数据聚类。通过将共聚嵌入到ABC算法用于聚类的目标函数中，我们将其合并。代替线性度量（例如欧几里得距离），我们建议使用更高阶的相关性来建立行和列之间的相似性，并且彼此相似。该度量使用共同演化的相似性，将其嵌入目标函数后可优化共同簇。还使用三种局部搜索方法在ABC算法产生的解的附近探索了搜索空间–第一种是基于计算聚类均值的启发式算法；第二种方法是使用物镜相对于质心的分析梯度来找到附近的低成本解决方案；第三，是前两种方法的混合。数值实验表明，通过结合新的相似性度量和优化的局部搜索方法，在寻找最佳聚类方面有了显着改进。最后，对于分布式和共享存储系统的并行体系结构，该算法都具有高度可伸缩性。理论上，最佳等效率函数是 对于分布式和共享存储系统的并行体系结构，该算法具有高度可伸缩性。理论上，最佳等效率函数是 对于分布式和共享存储系统的并行体系结构，该算法具有高度可伸缩性。理论上，最佳等效率函数是$\Theta$对于并行算法，还计算了具有p个处理器的全连接网络的（p log p）。