BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
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The objective of the present study is to investigate the possibility of developing an integrated database with information pertaining to the income of Italian families arising from two major surveys conducted by ISTAT EU-SILC and the Bank of Italy household income survey. Since neither of the surveys has the scope to allow for the construction of a database of information pertaining to income, an integration has been sought between the data from the two archives, assuming that the surveys are reliable in terms of the accuracy of the sample design and control of the representativeness of the sample. The development of our analysis is primarily aimed at carrying out an in-depth comparison between the two surveys in terms of structure, definition of variables and sample homogeneity and secondly, through the use of an integrated dataset, at a verification measurement of the validity of the information, in particular, of the income component.