Vol. 10 Issue 4
Year:2016
Issue:Apr-Jun
Title:Data Mining Approach For Advancement of “Association Rule Mining” Using “R Programming”
Author Name:Rahul Kumar Vij, Parveen Kalra and C.S. Jawalkar
Synopsis:
Over the years, data study was focused on finding proofs using rough datasets which made data mining to jump into, to show its strengths. This field gave the researchers a pictorial view of trends and patterns that can help in predictive maintenance. Algorithms available in past are unable to handle rough datasets; especially when major issues were processing data in advance and making an understandable model. Association rules were made to predict the correlation between the attributes. The current study mainly focuses on the trends followed by the “car evaluation dataset attributes” to give a suitable rating for a car. Arules library in “R” is used to reflect the same as well as some preprocessing is done. The results are then compared with “Step Induction Association Rule Mining” i.e. advancement done as well as orange classifier. The results clearly reflect that rules formed can help in predictive analysis of a car by knowing its features and specifications and the step induction rules give more clarity about the predictions.
Year:2016
Issue:Apr-Jun
Title:Data Mining Approach For Advancement of “Association Rule Mining” Using “R Programming”
Author Name:Rahul Kumar Vij, Parveen Kalra and C.S. Jawalkar
Synopsis:
Over the years, data study was focused on finding proofs using rough datasets which made data mining to jump into, to show its strengths. This field gave the researchers a pictorial view of trends and patterns that can help in predictive maintenance. Algorithms available in past are unable to handle rough datasets; especially when major issues were processing data in advance and making an understandable model. Association rules were made to predict the correlation between the attributes. The current study mainly focuses on the trends followed by the “car evaluation dataset attributes” to give a suitable rating for a car. Arules library in “R” is used to reflect the same as well as some preprocessing is done. The results are then compared with “Step Induction Association Rule Mining” i.e. advancement done as well as orange classifier. The results clearly reflect that rules formed can help in predictive analysis of a car by knowing its features and specifications and the step induction rules give more clarity about the predictions.
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