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A new clustering algorithm based on hybrid global optimization based on a dynamical systems approach algorithm

机译：基于动力系统进近算法的基于混合全局优化的新聚类算法

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Many methods for local optimization are based on the notion of a direction of a local descent at a given point. A local improvement of a point in hand can be made using this direction. As a rule, modern methods for global optimization do not use directions of global descent for global improvement of the point in hand. From this point of view, global optimization algorithm based on a dynamical systems approach (COP) is an unusual method. Its structure is similar to that used in local optimization: a new iteration can be obtained as an improvement of the previous one along a certain direction. In contrast with local methods, is a direction of a global descent and for more diversification combined with Tabu search. This algorithm is called hybrid GOP (HGOP). Cluster analysis is one of the attractive data mining techniques that are used in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means is used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies have been done in clustering. In this paper, we proposed application of hybrid global optimization algorithm based on a dynamical systems approach. We compared HGOP with other algorithms in clustering, such as GAK, SA, TS, and ACO, by implementing them on several simulation and real datasets. Our finding shows that the proposed algorithm works better than others.
机译：用于局部优化的许多方法都基于给定点处的局部下降方向的概念。使用该方向可以局部改善手头。通常，用于全局优化的现代方法不会使用全局下降的方向来全局改进手头的点。从这个角度来看，基于动态系统方法（COP）的全局优化算法是一种不寻常的方法。它的结构类似于局部优化中的结构：可以沿某个方向获得新的迭代作为对先前迭代的改进。与局部方法相反，是全局下降的方向，并且与禁忌搜索相结合可以实现更多样化的发展。该算法称为混合GOP（HGOP）。聚类分析是许多领域中使用的有吸引力的数据挖掘技术之一。一种流行的数据聚类算法是基于中心的聚类算法。由于K均值的简单性和对大型数据集进行聚类的高速性，它被用作一种流行的聚类方法。但是，K-means有两个缺点：依靠合理的计算量就无法找到对初始状态的依赖以及收敛于局部最优解和大问题的全局解。为了克服局部最优问题，已经在聚类中进行了许多研究。在本文中，我们提出了基于动态系统方法的混合全局优化算法的应用。通过将HGOP与GAK，SA，TS和ACO等聚类算法进行比较，我们将它们分别在几个模拟和真实数据集上进行了比较。我们的发现表明，提出的算法比其他算法更好。

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