The preprocessing in census data with concept hierarchy

Data Mining can extract implicit, previously unknown, and potentially useful information from data. Census is a significant investigation of national conditions and national power. Using Data Mining in census data has very high learning value and vast marketplace space. While census data are being collected and accumulated at dramatically high rates, concept hierarchy, one of the Data Mining techniques, can be used to reduce the data by collecting and replacing low-level concepts with higher-level concepts. Although detail is lost by such data generalization, the generalized data may be more meaningful and easier to interpret. Mining on a reduced data set can improve the quality of mining object and the obtained patterns after mining process. In this paper we apply concept hierarchy to preprocess the census data in Chengyang and Laixi, choose the dynamic concept hierarchy adjustment algorithm to adjust the obtained concept hierarchy on the attribute of “housing construction cost”, then evaluate the results, make preparation for the next step in mining process.