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首页> 外文期刊>International journal of systems assurance engineering and management >Hybrid Gbest-guided Artificial Bee Colony for hard partitional clustering
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Hybrid Gbest-guided Artificial Bee Colony for hard partitional clustering

机译：混合Gbest引导的人工蜂群用于硬分区聚类

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摘要

Clustering is an unsupervised classification method in the field of data mining and plays an essential role in applications in diverse fields. The K-means and K-Medoids are popular examples of conventional partitional clustering methods, which have been prominently applied in various applications. However, they possess several disadvantages, e.g., final solution is dependent on the initial solution, they easily struck into a local optimum solution. The nature-inspired swarm intelligence (SI) methods are global search optimization methods, which offer to be effective to overcome deficiencies of the conventional methods as they possess several desired key features like upgrading the candidate solutions iteratively, decentralization, parallel nature, and a self organizing behavior. The Artificial Bee Colony (ABC) algorithm is one of the recent and well-known SI method, which has been shown effective in various real-world problems. However, it exhibits lack of balance in the exploration and exploitation and shows a poor convergence speed when the number of features (dimensions) increases. Therefore, we make two modifications in it to enhance its exploration and exploitation capabilities to improve quality of the clustering. First, we introduce a gbest-guided search procedure for the fast convergence, which works effectively in large number of features also as it considers all the dimensions simultaneously. Second, in order to avoid being trapped in a local optima and to enhance the information exchange (social learning) between bees for improved search, we incorporate a crossover operator of the genetic algorithm (GA) into it. The proposed strategy is named as Hybrid Gbest-guided Artificial Bee Colony (HGABC) algorithm. We compare clustering results of the HGABC with ABC, variants of the ABC and other recent competitive methods in the swarm and evolutionary intelligence domain on ten real and two synthetic data sets using external quality measures F-measure and Rand-index. The obtained results demonstrate superiority of the proposed method over its competitors in terms of efficiency and effectiveness.
机译：聚类是数据挖掘领域中的一种无监督分类方法，在不同领域的应用中起着至关重要的作用。 K-means和K-Medoids是常规分区聚类方法的流行示例，已在各种应用程序中得到显着应用。但是，它们具有几个缺点，例如，最终解决方案取决于初始解决方案，它们很容易成为局部最优解决方案。受自然启发的群智能（SI）方法是全局搜索优化方法，可有效克服常规方法的不足，因为它们具有若干所需的关键功能，例如迭代地升级候选解决方案，去中心化，并行性质和自我。组织行为。人工蜂群（ABC）算法是最近的一种众所周知的SI方法，已被证明在各种现实问题中均有效。然而，当特征（维数）增加时，它在勘探和开发中缺乏平衡，并且收敛速度较差。因此，我们对其进行了两次修改，以增强其勘探和开发能力，以提高聚类的质量。首先，我们为快速收敛引入了gbest指导的搜索过程，该过程可同时考虑所有维度，因此在许多功能中均有效。其次，为了避免陷入局部最优状态并增强蜜蜂之间的信息交换（社会学习）以改善搜索，我们将遗传算法（GA）的交叉算子纳入其中。所提出的策略称为混合Gbest引导的人工蜂群（HGABC）算法。我们使用外部质量测度F-measure和Rand-index在10个真实和两个合成数据集上比较了HGABC与ABC，ABC的变体以及其他最新竞争方法在群体和进化情报领域的聚类结果。获得的结果证明了该方法在效率和有效性方面优于其竞争对手。

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