Title: Two-phase data types transformation framework in data mining
Authors: Jiang, MF
Tseng, SS
Liao, SY
Chen, WC
資訊工程學系
Department of Computer Science
Issue Date: 2001
Abstract: As we know, the data processed in data mining may be obtained from many sources in which different data types may be used. However, no algorithm can be applied to all applications due to the difficulty for fitting data types of the algorithm, so the selection of an appropriate mining algorithm is based on not only the goal of application, but also the data fittability. Therefore, transforming the non-fitting data type into target one is also an important work in data mining, but the work is often tedious or complex since a lot of data types exist in real world. Merging the similar data types of a given selected mining algorithm into a generalized data type seems to be a good approach to reduce the transformation complexity. In this work, a two-phase data types transformation framework including merging and transforming phases is proposed. With the data type transformation framework, the user can select appropriate mining algorithm iterative and interactive for the goal of application without considering the data types.
URI: http://hdl.handle.net/11536/19000
ISBN: 1-58603-192-9
ISSN: 0922-6389
Journal: KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2
Volume: 69
Begin Page: 490
End Page: 494
Appears in Collections:Conferences Paper


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