
AN EFFICIENT APPROACH USING RULE INDUCTION AND ASSOCIATION RULE MINING ALGORITHMS IN DATA MINING
Author(s) -
Kapil Sharma,
Sheveta Vashisht
Publication year - 2013
Publication title -
graduate research in engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2320-6632
DOI - 10.47893/gret.2013.1021
Subject(s) - association rule learning , rule induction , data mining , computer science , decision rule , entropy (arrow of time) , set (abstract data type) , algorithm , artificial intelligence , physics , quantum mechanics , programming language
In this research work we use rule induction in data mining to obtain the accurate results with fast processing time. We using decision list induction algorithm to make order and unordered list of rules to coverage of maximum data from the data set. Using induction rule via association rule mining we can generate number of rules for training dataset to achieve accurate result with less error rate. We also use induction rule algorithms like confidence static and Shannon entropy to obtain the high rate of accurate results from the large dataset. This can also improves the traditional algorithms with good result.