scholarly journals A Data Mining Approach for Prediction Modeling Using Association rule

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Sarkhel H.Taher Karim ◽  
Rzgar Sirwan Raza
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Optimization Problems ◽  
Analysis Model ◽  
Prediction Modeling ◽  
Prediction Analysis ◽  
Data Mining Approach

   In the present work, a data mining approach is highlighted, a prediction optimization data mining approach association rule is chosen for performing prediction modeling in a supermarket application, a data mining prediction analysis model is formulated based on association rule is presented in this work.  The result of the model formulated is then compared with the result produced on the similar set of input on the traditional optimization problems. While comparing the results it was observed that the result produced by the presented model is much closer to the reality.

An Association Rule on eDisiplin Case Study: An Educational Data Mining Approach

2018 ◽  
Vol 24 (3) ◽  
pp. 1872-1875 ◽  
Author(s):  
Mustafa Man ◽  
Wan Aezwani Wan Abu Bakar ◽  
Ily Amalina Ahmad Sabri
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Educational Data Mining ◽  
Data Mining Approach

DATA MINING APPROACH FOR ADVANCEMENT OF “ASSOCIATION RULE MINING” USING “R PROGRAMMING”

2016 ◽  
Vol 10 (4) ◽  
pp. 6
Author(s):  
VIJ RAHUL KUMAR ◽  
KALRA PARVEEN ◽  
JAWALKAR C.S. ◽  
◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Approach ◽  
R Programming

scholarly journals Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 309-316
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Healthcare Data ◽  
Data Mining Approach ◽  
Mining Community ◽  
Pattern Growth ◽  
Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

scholarly journals Efficient Method in Association Rule Hiding for Privacy Preserving with Data Mining Approach

2019 ◽  
Vol 24 (1) ◽  
pp. 47-50
Author(s):  
Kurapati Praveena ◽  
Gudla Sirisha ◽  
Satukumati Babu ◽  
Panchala Rao
Keyword(s):  
Data Mining ◽  
Efficient Method ◽  
Association Rule ◽  
Privacy Preserving ◽  
Data Mining Approach
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