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Please use this identifier to cite or link to this item: http://elib.bsu.by/handle/123456789/50890
Title: Developing Multiple Sub Functions Per Association Function For Data Mining System
Authors: Sivakumar, R.
Meena, K.
Keywords: ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
ЭБ БГУ::ОБЩЕСТВЕННЫЕ НАУКИ::Информатика
Issue Date: 2004
Publisher: Минск: БГУ
Abstract: Most of the existing data mining systems specialize in one data mining functions or just one approach of data mining functions. But many problems may require users to try a few different minimum functions so that they can be shown to be more effective for different kinds of data. Hence this paper focuses on introducing multiple sub functions per association function in data mining tools. To fulfill this requirement, it designs functions for discovering multiple level associations rules, two-dimensional optimized gain rules and synthesized association rules from huge databases. The intelligent performance of existing systems can be improved by the incorporation of such added features.
URI: http://elib.bsu.by/handle/123456789/50890
Appears in Collections:2004. Международная конференция “Моделирование процессов и систем”

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