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

Dale Dzemydiene, Vitalija Rudzkiene

    Research output: Contribution to journalArticle

    5 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)471-486
    Number of pages16
    JournalInformatica (Netherlands)
    Issue number4
    Publication statusPublished - 2003



    • Criminality
    • Data mining
    • Data warehouse
    • Multidimensional statistical methods
    • Social-economical indicators

    ASJC Scopus subject areas

    • Information Systems
    • Applied Mathematics

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