Robust Region Grouping via Internal Patch Statistics

2013 ◽  
Author(s):  
Xiaobai Liu ◽  
Liang Lin ◽  
Alan L. Yuille
Keyword(s):  
Region Grouping

scholarly journals Analisa Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018

2019 ◽  
Vol 8 (2) ◽  
pp. 130
Author(s):  
Nugroho Irawan Febianto ◽  
Nicodias Palasara
Keyword(s):  
Clustering Method ◽  
Poverty Level ◽  
West Java ◽  
Characteristic Mapping ◽  
Solid Foundation ◽  
The Government ◽  
Poverty Indicators ◽  
The Right ◽  
Region Grouping

Abstract— Poverty is a condition of life that is understaffed by a person or household so that it is unable to meet the minimum or proper needs for his or her life. The poverty Data in each region will differ. It is influenced by many of its supporting indicators. By determining and measuring the indicators of poverty, it will facilitate and recognize the poverty level of the region. Grouping characteristics of a region based on poverty indicators, so that the government can precisely and quickly take policies to mitigate poverty in a region. The method used in this study uses the K-Means Clustering method. The Clustering method is selected because this method has the ability to classify large amounts of data with faster process times efficiently. The object in this study used data published by the BPS (Badan Pusat Statistik) on poverty Data and information in the Regency/city in 2018. Based on the results of this study, the results of the characteristic mapping of each group formed based on the highest and lowest value of poverty indicator of West Java province year 2018. With the characteristics found in each region, it will certainly be a solid foundation for government organizers to provide the right and quick policy/approach to overcome the poverty that is found in the region.

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