Feature selection and knowledge extraction from buppu 2000 and 2010 censuses using artificial intelligence

The census of population and dwellings undertaken by national state institutions world over at regular time intervals, is a fantastic source of information. However, there are major challenges to overcome when transforming the census data that usually consists of a vast number of attributes, into useful knowledge. In this paper, an artificial intelligent approach is investigated to select appropriate attribute features that indicate interesting patterns in Beppu census wards in 2000 and 2010. The results of the SOM (unsupervised artificial neural network) based clustering, GIS visualisation and machine learning (J48 and JRip functions of WEKA), provide relevant discerning features, new patterns and new knowledge that can be of use in many application domains, such as urban planning, resources management and transport planning.