Equivalence of Transforming Non-Linear DACG to Linear Concept Tree

Junping Sun; Wenyi Bi

doi:10.1109/icis.2005.61

Equivalence of Transforming Non-Linear DACG to Linear Concept Tree

Junping Sun
, Wenyi Bi

Research output: Contribution to conference › Paper

1 Scopus citations

Abstract

This paper introduces a strategy and its theory proof to transform non-linear concept graph: Directed Acyclic Concept Graph (DACG) into a linear concept tree. The transformation is divided into three steps: normalizing DACG into a linear concept tree, establishing a function on host attribute, and reorganizing the sequence of concept generalization. This study develops alternative approach to discovery knowledge under non-linear concept graph. It overcomes the problems with information loss in rule-based attribute oriented induction and low efficiency in path-id method. Because DACG is a more general concept schema, it is able to extract rich knowledge implied in different directions of non-linear concept scheme.

Original language	American English
Pages	86-93
Number of pages	8
DOIs	https://doi.org/10.1109/icis.2005.61
State	Published - Jul 1 2005
Event	Fourth International Conference on Computer and Information Science - Duration: Jul 1 2005 → …

Conference

Conference	Fourth International Conference on Computer and Information Science
Period	7/1/05 → …

ASJC Scopus Subject Areas

General Engineering

Disciplines

Computer Sciences

Access to Document

10.1109/icis.2005.61

Cite this

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title = "Equivalence of Transforming Non-Linear DACG to Linear Concept Tree",

abstract = " This paper introduces a strategy and its theory proof to transform non-linear concept graph: Directed Acyclic Concept Graph (DACG) into a linear concept tree. The transformation is divided into three steps: normalizing DACG into a linear concept tree, establishing a function on host attribute, and reorganizing the sequence of concept generalization. This study develops alternative approach to discovery knowledge under non-linear concept graph. It overcomes the problems with information loss in rule-based attribute oriented induction and low efficiency in path-id method. Because DACG is a more general concept schema, it is able to extract rich knowledge implied in different directions of non-linear concept scheme.",

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AB - This paper introduces a strategy and its theory proof to transform non-linear concept graph: Directed Acyclic Concept Graph (DACG) into a linear concept tree. The transformation is divided into three steps: normalizing DACG into a linear concept tree, establishing a function on host attribute, and reorganizing the sequence of concept generalization. This study develops alternative approach to discovery knowledge under non-linear concept graph. It overcomes the problems with information loss in rule-based attribute oriented induction and low efficiency in path-id method. Because DACG is a more general concept schema, it is able to extract rich knowledge implied in different directions of non-linear concept scheme.

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Equivalence of Transforming Non-Linear DACG to Linear Concept Tree

Abstract

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