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INFORMATICA, 2003, Vol. 14, No. 4, 471-486
© Institute of Mathematics and Informatics,

ISSN 0868-4952

Data Analysis Strategy for Revealing Multivariate Structures in Social-Economic Data Warehouses

Dale DZEMYDIENEa, Vitalija RUDZKIENEb

aDepartment of Software Engineering, Institute of Mathematics and Informatics Akademijos 4, LT-2021 Vilnius, Lithuania Department of Legal Informatics, Law University of Lithuania Ateities 20, LT-2057 Vilnius, Lithuania E-mail: daledz@ktl.mii.lt

bDepartment of Legal Informatics, Law University of Lithuania Ateities 20, LT-2057 Vilnius, Lithuania E-mail: vital@ltu.lt

Abstract

This research work is aimed at the development of data analysis strategy in a complex, multidimensional, and dynamic domain. Our universe of discourse is concerned with the data mining techniques of data warehouses revealing the importance of multivariate structures of social-economic data which influence criminality. Distinct tasks require different data structures and various data mining exercises in data warehouses. The proposed problem solution strategy allows choosing an appropriate method in recognition processes. The ensembles of diverse and accurate classifiers are constructed on the base of multidimensional classification and clusterisation methods. Factor analysis is introduced into data mining process for revealing influencing impacts of factors. The temporal nature and multidimensionality of the target object is revealed in dynamic model using multidimension regression estimates. The paper describes the strategy of integrating the methods of multiple statistical analysis in cases, where a great set of variables is observed in short time period. The demonstration of the data analysis strategy is performed using real social and economic development of data warehouses in different regions of Lithuania.

Keywords:

multidimensional statistical methods, data mining, data warehouse, social-economical indicators, criminality

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