Entropy Based Rule Derivation from Data with Uncertainty

Junping Sun

doi:10.1109/fuzz.2001.1009062

Entropy Based Rule Derivation from Data with Uncertainty

Junping Sun

Research output: Contribution to journal › Conference article

Abstract

Due to its advantages, fuzzy data model has been widely used to model and represent data with uncertainty. More and more applications show the needs to explore the data with uncertainty and to perform tasks of knowledge discovery in fuzzy database. This paper presents an attribute-oriented and probabilistic entropy based approach to knowledge discovery from uncertain data. The probabilistic entropy with the weighted values of membership functions is used to measure the possibility from fuzzy data sets. Also, it is employed to derive the rules that characterize these data sets.

Original language	American English
Pages (from-to)	744-748
Number of pages	5
Journal	Proceedings of the 10th IEEE International Conference on Fuzzy Systems
DOIs	https://doi.org/10.1109/fuzz.2001.1009062
State	Published - Aug 25 2005

ASJC Scopus Subject Areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Disciplines

Computer Sciences

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10.1109/fuzz.2001.1009062

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title = "Entropy Based Rule Derivation from Data with Uncertainty",

abstract = " Due to its advantages, fuzzy data model has been widely used to model and represent data with uncertainty. More and more applications show the needs to explore the data with uncertainty and to perform tasks of knowledge discovery in fuzzy database. This paper presents an attribute-oriented and probabilistic entropy based approach to knowledge discovery from uncertain data. The probabilistic entropy with the weighted values of membership functions is used to measure the possibility from fuzzy data sets. Also, it is employed to derive the rules that characterize these data sets.",

author = "Junping Sun",

year = "2005",

month = aug,

day = "25",

doi = "10.1109/fuzz.2001.1009062",

language = "American English",

pages = "744--748",

journal = "Proceedings of the 10th IEEE International Conference on Fuzzy Systems",

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T1 - Entropy Based Rule Derivation from Data with Uncertainty

AU - Sun, Junping

PY - 2005/8/25

Y1 - 2005/8/25

N2 - Due to its advantages, fuzzy data model has been widely used to model and represent data with uncertainty. More and more applications show the needs to explore the data with uncertainty and to perform tasks of knowledge discovery in fuzzy database. This paper presents an attribute-oriented and probabilistic entropy based approach to knowledge discovery from uncertain data. The probabilistic entropy with the weighted values of membership functions is used to measure the possibility from fuzzy data sets. Also, it is employed to derive the rules that characterize these data sets.

AB - Due to its advantages, fuzzy data model has been widely used to model and represent data with uncertainty. More and more applications show the needs to explore the data with uncertainty and to perform tasks of knowledge discovery in fuzzy database. This paper presents an attribute-oriented and probabilistic entropy based approach to knowledge discovery from uncertain data. The probabilistic entropy with the weighted values of membership functions is used to measure the possibility from fuzzy data sets. Also, it is employed to derive the rules that characterize these data sets.

UR - https://nsuworks.nova.edu/gscis_facarticles/493

U2 - 10.1109/fuzz.2001.1009062

DO - 10.1109/fuzz.2001.1009062

M3 - Conference article

SP - 744

EP - 748

JO - Proceedings of the 10th IEEE International Conference on Fuzzy Systems

JF - Proceedings of the 10th IEEE International Conference on Fuzzy Systems

ER -

Entropy Based Rule Derivation from Data with Uncertainty

Abstract

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