This study proposes a data-mining analysis method for examining variable multi-attribute big-data, which is considered to be more applicable in social science using a Correspondence Analysis of variables obtained by AIC model selection. The proposed method was applied on the Seoul Survey from 2005 to 2010 in order to extract interesting rules or patterns on characteristics of single household. The results found as follows. Firstly, this paper illustrated that the proposed method is efficiently able to apply on a big dataset of huge categorical multi attributes variables. Secondly, as a result of Seoul Survey analysis, it has been found that the more dissatisfied with residential environment the higher tendency of residential mobility in single household. Thirdly, it turned out that there are three types of single households based on the characteristics of their demographic characteristics, and it was different from recognition of home and partner of counselling by the three types of single households. Fourthly, this paper extracted eight significant variables with a spatial aggregated dataset which are highly correlated to the ratio of occupancy of single household in 25 Seoul Municipals, and to conclude, it investigated the relation between spatial distribution of single households and their demographic statistics based on the six divided groups obtained by Cluster Analysis.