GDL

Journal from gdlhub / 2017-08-14 08:59:00
Oleh : Nila Nurmala, Ayu Purwarianti, ITB
Dibuat : 2017-04-15, dengan 1 file

Keyword : ant colony optimization, clustering, context-based clustering, CUDA, geodemographic analysis
Url : http://journals.itb.ac.id/index.php/jictra/article/view/2936
Sumber pengambilan dokumen : Web

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.

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