Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules

Intan, Rolly; Yuliana, Oviliani Yenty

doi:10.1007/978-3-642-10684-2_80

Search citation statements

Order By: Relevance

Paper Sections

Select...

Confidence Degrees1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2010

2021

Publication Types

Select...

Article3

Book1

Other1

Relationship

Self Cite1

Independent4

Authors

Journals

Cited by 5 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once the membership degrees are computed, the FID3 algorithm follows the three main steps summarized in Algorithm 2 (Mitra et al, 2002;Jin et al, 2007;Intan and Yuliana, 2009;Chen and Shie, 2009): Algorithm 2. Fuzzy ID3 1.…”

Section: Confidence Degreesmentioning

confidence: 99%

A data mining based approach to predict spatiotemporal changes in satellite images

Boulila

Farah

Ettabaa

et al. 2011

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

Section: Confidence Degreesmentioning

confidence: 99%

A data mining based approach to predict spatiotemporal changes in satellite images

Boulila

Farah

Ettabaa

et al. 2011

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

Fuzzy Bayesian Belief Network for Analyzing Medical Track Record

Intan

Yuliana

2010

Advances in Intelligent Information and Database Systems

Self Cite

View full text Add to dashboard Cite

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, the BBN concept is extended with meaningful fuzzy labels for mining fuzzy association rules. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels secondary disease and complication disease are defined for disease classification. We extend the concept of Mutual Information dealing with fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining association among nodes. A brief algorithm is introduced to develop the proposed concept. Experimental results of the algorithm show processing time in the relation to the number of records and the number of nodes. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area.

show abstract