Affiliations: [a] Data processing and Dissemination Department, Statistical Research and Training Center, Tehran, Iran | [b] School of Science, Engineering and Environment, Salford, Manchester, UK
Correspondence:
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Corresponding author: Abbas Moradi, Data processing and Dissemination Department, Statistical Research and Training Center, Tehran, Iran. E-mail: amoradi@srtc.ac.ir.
Abstract: The big data sources of National Statistical Offices (NSOs) are provided to make a superior platform for decision-making. The household income and expenditure survey is one of the economically important surveys especially when the inflation rate varies to assess the changes in households’ consumption patterns. In this case, big data can be beneficial and help to accurately measure consumption patterns of urban and rural households at every geographical level. This analysis is an exploratory study for the extraction of the size of injustice and imparity of household income and facilities implemented by classifying and clustering all Iranian households. Through this study, classification and soft clustering (Fuzzy clustering) techniques are employed to characterize the Iranian household types from 2011 to 2021, which are supervised and unsupervised approaches, respectively. Moreover, association rule mining techniques are employed to discover and extract consumption patterns for each cluster. Obtained results showed that there was a significant gap between purchasing power/receiving energy between lowest and highest income households from 2011 to 2021, and this gap is increasing day by day.
Keywords: Data mining, official statistics, classification, soft clustering, association rules
