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

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 Parametric Rough Sets with Application to Granular Association Rule Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xu He ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Recommender Systems ◽  
Association Rule ◽  
Rough Sets ◽  
Relational Databases ◽  
Data Sets ◽  
Rule Mining ◽  
Real World Data ◽  
New Model ◽  
New Type ◽  
Two Universes

Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable.

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.

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