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Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization using context-based clustering
Nurmala N.a, Purwarianti A.a
a School of Electrical Engineering and Informatics, Institute of Technology Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.Geo-demographic analysis (GDA) is an interdisciplinary that studies characteristics of population based on geographical area. Fuzzy Geographically Weighted Clustering-Ant Colony Optimization (FGWC-ACO), which is the improvement of FGWC algorithm, is considered as an effective algorithm in GDA. The integration of Ant Colony Optimization (ACO) as a metaheuristic algorithm to the FGWC has been used as a global optimization tools to improve geo-demographic clustering accuracy in initial phase. Nonetheless, using ACO makes the computation running time of FGWC-ACO is slower than standard FGWC. In this paper, we propose a method to attach context variables to FGWC-ACO in order to accelerate the computing speed of the algorithm and to focus the clustering result on a certain condition. An experiment of the proposed method has been done using Indonesia Population Census 2010 from Statistics Indonesia to prove that the proposed method can improve the computing speed of FGWC-ACO and using IFV index as a validity index for spatial fuzzy clustering to evaluate the clustering quality of proposed method.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Ant Colony Optimization (ACO),clustering,Context-based,Demographic clustering,fuzzy geographically weighted clustering,geo-demographic analysis,Meta heuristic algorithm,Spatial fuzzy clustering[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ant colony optimization,clustering,context-based clustering,fuzzy geographically weighted clustering,geo-demographic analysis[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICITSI.2015.7437726[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]