文章基本信息

标题：Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming
本地全文：下载
作者：Nila Nurmala ; Ayu Purwarianti
期刊名称：Journal of ICT Research and Applications
印刷版ISSN：2337-5787
电子版ISSN：2338-5499
出版年度：2017
卷号：11
期号：1
页码：21-37
语种：English
出版社：Institut Teknologi Bandung
其他摘要：Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Context-based clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.