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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >HFSMOOK-Means: An Improved K-Means Algorithm Using Hesitant Fuzzy Sets and Multi-objective Optimization
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HFSMOOK-Means: An Improved K-Means Algorithm Using Hesitant Fuzzy Sets and Multi-objective Optimization

机译：HFSMook-mease：一种使用犹豫模糊集和多目标优化的改进的K均值算法

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摘要

Clustering is considered as one of the important methods in data mining. The performance of the K-means algorithm, as one of the most common clustering methods, is high sensitivity to the initial cluster centers. Hence, selecting appropriate initial cluster centers for implementing the algorithm improves clustering resulted from the algorithm. The present study aims to find suitable initial cluster centers for the K-means. In fact, the initial cluster centers should be selected in such a way that clusters with high separation and high density can be obtained. Therefore, in this paper, finding initial cluster centers is considered as a multi-objective optimization problem through maximizing the distance between the initial cluster centers, as well as the neighbor density of the initial cluster centers. Solving the above problem through using the MOPSO algorithm provided a set of initial cluster centers of the candidate. Then, the hesitant fuzzy sets were used to evaluate the clusters generated from initial cluster centers by considering separation, cohesion and silhouette index. After that, the concept of informational energy of hesitant fuzzy sets is used, by which non-dominated particles in the Pareto optimal set were ranked and the initial cluster centers were selected for starting the K-means algorithm. The proposed HFSMOOK-means method was compared with several clustering algorithms by considering common and widely used criteria. The results indicated the successful performance of HFSMOOK-means in the majority of the datasets compared to the other algorithms.
机译：聚类被认为是数据挖掘中的重要方法之一。 K-Means算法的性能作为最常见的聚类方法之一，对初始集群中心具有很高的灵敏度。因此，选择用于实现该算法的适当初始群集中心改善了算法产生的聚类。本研究旨在为k型方式找到合适的初始集群中心。事实上，应以这样的方式选择初始聚类中心，可以获得具有高分离和高密度的簇。因此，在本文中，通过最大化初始集群中心之间的距离以及初始集群中心的邻居密度，发现初始集群中心被认为是多目标优化问题。通过使用MOPSOS算法来解决上述问题提供了一组候选者的一组初始集群中心。然后，犹豫不决的模糊集用于通过考虑分离，凝聚和轮廓索引来评估初始聚类中心产生的簇。之后，使用犹豫不决模糊组的信息能量的概念，通过该帕累托最佳集合中的非主导粒子被排序，并且选择初始群集中心用于启动K-Means算法。通过考虑常见和广泛使用的标准，将所提出的HFSMook-Use方法与几种聚类算法进行比较。结果表明，与其他算法相比，在大多数数据集中的HFSmook-ilse的成功表现。

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