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首页> 外文期刊>American journal of applied sciences >Using Hyper Clustering Algorithms in Mobile Network Planning
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Using Hyper Clustering Algorithms in Mobile Network Planning

机译：在移动网络规划中使用超聚类算法

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Problem statement: As a large amount of data stored in spatial databases, people may like to find groups of data which share similar features. Thus cluster analysis becomes an important area of research in data mining. Applications of clustering analysis have been utilized in many fields, such as when we search to construct a cluster served by base station in mobile network. Deciding upon the optimum placement for the base stations to achieve best services while reducing the cost is a complex task requiring vast computational resource. Approach: This study addressed antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks by modified the original density-based Spatial Clustering of Applications with Noise algorithm. The Cluster Partitioning around Medoids original algorithm had been modified and a new algorithm had been proposed by the authors in a recent work. The density-based Spatial Clustering of Applications with Noise original algorithm had been modified and combined with old algorithm to produce the hybrid algorithm Clustering Density Base and Clustering with Weighted Node-Partitioning around Medoids algorithm to solve the problems in Mobile Network Planning. Results: Implementation of this algorithm to a real case study wa Results demonstrate that the proposed algorithm s presented. Results demonstrate that the proposed algorithm had minimum run time minimum cost and high grade of service. Conclusion: The proposed hyper algorithm has the advantage of quick divide the area into clusters where density base algorithm has a limit iteration and the advantage of accuracy (no sampling method is used) and highly grade of service due to the moving of the location of the base stations (medoid) toward the heavy loaded (weighted) nodes.
机译：问题陈述：由于大量存储在空间数据库中的数据，人们可能喜欢查找共享相似特征的数据组。因此，聚类分析成为数据挖掘研究的重要领域。聚类分析的应用已在许多领域得到利用，例如当我们搜索构建移动网络中基站服务的聚类时。确定基站的最佳位置以实现最佳服务并降低成本是一项复杂的任务，需要大量的计算资源。方法：这项研究解决了天线放置问题或小区规划问题，涉及通过修改原始的基于密度的应用噪声的应用程序空间聚类来定位和配置移动网络的基础设施。作者在最近的工作中对基于Medoids的聚类划分进行了修改，并提出了一种新算法。对基于噪声的应用程序的基于空间的空间聚类进行了修改，并与旧算法相结合，以产生混合密度聚类算法和加权节点分区聚类算法聚类算法，解决了移动网络规划中的问题。结果：该算法在实际案例中的实现与实验结果表明，提出了该算法。结果表明，该算法具有运行时间最小，成本最小，服务等级高的特点。结论：所提出的超级算法的优点是将区域快速划分为簇，密度基算法具有有限的迭代次数，并且由于位置的移动而具有准确性（不使用采样方法）和服务等级高的优点。朝向重载（加权）节点的基站（medoid）。

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