Title :
Enhancement of associative rule based FOIL and PRM algorithms
Author :
Rai, Devashree ; Thoke, A.S. ; Verma, Keshri
Author_Institution :
Dept. of Electr. Eng., Nat. Inst. of Technol., Raipur, India
Abstract :
Classification is one of the important problems in Data Mining. There are various methods of classification available like Rule based method, Decision tree, Neural network, and Bayesian networks. This paper focuses on Associative rule based classifier. FOIL (First order Inductive Learner) and PRM (Predictive Rule Mining) algorithms have been analysed in this work. The proposed work is superior to reported works in terms of memory requirements by eliminating use of intermediate data structure without sacrificing classification accuracy. The proposed work is an enhancement of existing FOIL and PRM algorithms.
Keywords :
belief networks; data analysis; data mining; decision trees; learning (artificial intelligence); neural nets; pattern classification; Bayesian networks; FOIL algorithm; PRM algorithm; associative rule enhancement; classification method; data mining; decision tree; first order inductive learner algorithm; intermediate data structure usage elimination; memory requirements; neural network; predictive rule mining algorithm; rule based method; training data analysis; Accuracy; Algorithm design and analysis; Classification algorithms; Data mining; Machine learning algorithms; Memory management; Prediction algorithms; Association rules; classification; data mining; first order inductive learner (FOIL) algorithm; predictive rule mining (PRM) algorithm;
Conference_Titel :
Engineering and Systems (SCES), 2012 Students Conference on
Conference_Location :
Allahabad, Uttar Pradesh
Print_ISBN :
978-1-4673-0456-6
DOI :
10.1109/SCES.2012.6199095
