Open AccessRegression Model Method for Analyze the Association Rules using Major Parameters
Author(s) -
Gaurang Shah,
Hiral Patel
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g1024.0597s20
Subject(s) - association rule learning , computer science , data mining , task (project management) , set (abstract data type) , confidence interval , linear regression , regression analysis , association (psychology) , regression , value (mathematics) , artificial intelligence , machine learning , statistics , mathematics , engineering , philosophy , systems engineering , epistemology , programming language
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