Using Hyper Clustering Algorithms in Mobile Network Planning
Lamiaa Fattouh Ibrahim and Hesham A. Salman
DOI : 10.3844/ajassp.2011.1004.1013
American Journal of Applied Sciences
Volume 8, Issue 10
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 addresses 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 has been modified and a new algorithm has been proposed by the authors in a recent work. In this study, the density-based Spatial Clustering of Applications with Noise original algorithm has 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 is presented. Results demonstrate that the proposed algorithm has 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 the 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.
© 2011 Lamiaa Fattouh Ibrahim and Hesham A. Salman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.