A Study of Association Rule Generation by Interval Estimator of Information Component

2021 ◽  
Vol 23 (3) ◽  
pp. 1063-1074
Author(s):  
Hee-Chan Park
Keyword(s):  
Association Rule ◽  
Rule Generation ◽  
Information Component ◽  
Interval Estimator

Association rule-generation algorithm for mining automotive warranty data

2006 ◽  
Vol 44 (14) ◽  
pp. 2749-2770 ◽  
Author(s):  
J. Buddhakulsomsiri ◽  
Y. Siradeghyan ◽  
A. Zakarian ◽  
X. Li
Keyword(s):  
Association Rule ◽  
Rule Generation ◽  
Generation Algorithm

scholarly journals Selective association rule generation

2007 ◽  
Vol 23 (2) ◽  
pp. 303-315 ◽  
Author(s):  
Michael Hahsler ◽  
Christian Buchta ◽  
Kurt Hornik
Keyword(s):  
Association Rule ◽  
Rule Generation

scholarly journals An Effectual Ga Based Association Rule Generation and Fuzzy Svm Classification Algorithm for Predicting Students Performance

2019 ◽  
Vol 8 (6) ◽  
pp. 2915-2920
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Association Rule ◽  
Educational Data Mining ◽  
Rule Generation ◽  
Academic Status ◽  
Student Records ◽  
Performance Simulation ◽  
Svm Classification ◽  
Student’S Performance

This investigation provides outcome of utilizing educational data mining [EDM] to design academic performance of students from real time and online dataset collected from colleges. Data mining is determined to examine non-academic and academic data; this model utilizes a classification approach termed as Fuzzy SVM classification with Genetic algorithm to attain effectual understanding of association rule in enrolment and to evaluate data quality for classification, which is identified as prediction task of performance and academic status based on low academic performance. This model attempts to predict student’s performance in grading system. Academic and student records attained from process were considered to train models estimated using cross-validation and formerly records from complete academic performance. Simulation was performed in MATLAB environment and show that academic status prediction is enhanced while hybrid dataset are added. The accuracy was compared with the existing models and shows better trade off than those methods.

scholarly journals Association Rule Generation using Modified Hashing Function

2014 ◽  
Vol 95 (1) ◽  
pp. 33-36
Author(s):  
M. Ramakrishnana ◽  
D. Tennyson Jyaraj
Keyword(s):  
Association Rule ◽  
Rule Generation ◽  
Hashing Function

Rough Sets and Association Rule Generation

1999 ◽  
Vol 40 (4) ◽  
pp. 383-405 ◽  
Author(s):  
Hung Son Nguyen ◽  
Sinh Hoa Nguyen
Keyword(s):  
Association Rule ◽  
Rough Sets ◽  
Rule Generation
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