Regression Model Method for Analyze the Association Rules using Major Parameters
G. G. Shah1, H. N. Patel2
1G. G. Shah, Faculty of Business Administration, Dharmsinh Desai University, Nadiad, India.
2Dr. H. N. Patel, Department of Computer Science, Dr. Babasaheb Ambedker Open University, Ahmedabad, India.
Manuscript received on 26 April 2020 | Revised Manuscript received on 08 May 2020 | Manuscript Published on 22 May 2020 | PP: 71-74 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10240597S20 | DOI: 10.35940/ijitee.G1024.0597S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Using the data mining user can extract the information. Frequent itemsets is one of the popular task in data mining. Association Rule Analysis is the task of discovering association rules that occur frequently in a given large data set.The task is to find certain relationships among a set of itemsets in the database. There are two fundamental parameter(measurement) is Support and Confidence.Traditional association rule mining techniques employ predefined support and confidence values. But, it’s observed that specifying minimum support value of the minded rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system.This paper proposes a non-linear regression model using support, confidence and association rules. To predict the number of rules under the given explanatory variables say parameters. Use the R language for the Rules generations and also uses significance test to verify regression coefficients. Using the coefficient test and F-test verify the model.
Keywords: Association Rules, Regression, Regression Coefficients, Multiple Correlation, F-Test.
Scope of the Article: Open Models and Architectures