@article {441,
	title = {Resolving rule conflicts based on Na{\"\i}ve Bayesian model for associative classification},
	journal = {Journal of Digital Information Management},
	volume = {12},
	year = {2014},
	note = {Export Date: 10 July 2014},
	month = {2014},
	pages = {36 - 43},
	abstract = {The rule conflict is an important issue for associative classification due to a large set of rules. In this paper, a new approach called Associative Classification with Bayes (AC-Bayes) is proposed. To address rule conflicts, AC-Bayes has two distinguished features: (1) Associative classification is improved. (2) Na{\"\i}ve Bayesian model is applied in process of classification. A small set of high quality rules is generated by discovering not only frequent but mutual associated itemsets. Therefore, it will reduce the opportunities of rule conflicts. AC-Bayes also selects the best n rules to predict the class value of new instances. When rule conflicts are occurred, we collect those instances covered by these matched rules to form a new training set. Then, it scans new training set to compute the posterior probabilities of each class conditioned on the test instance. The class value that maximizes the posterior probability is assigned to the test instance. The experiments{\textquoteright} results show that the improved associative classification decreases significantly the number of rules and AC-Bayes has better average classification accuracy in comparison with associative classification and Na{\"\i}ve Bayesian classification.},
	keywords = {Associative classification, Bayes, Data mining, Rule conflicts},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84900460217\&partnerID=40\&md5=bec565675923a67f04838dadf519210d},
	author = {Huang, Z. and Zhou, Z. and He, T.}
}
@article {55,
	title = {Pruning techniques in associative classification: Survey and comparison},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Cited By (since 1996):6Export Date: 10 July 2014},
	month = {2006},
	pages = {197 - 202},
	abstract = {Association rule discovery and classification are common data mining tasks. Integrating association rule and classification also known as associative classification is a promising approach that derives classifiers highly competitive with regards to accuracy to that of traditional classification approaches such as rule induction and decision trees. However, the size of the classifiers generated by associative classification is often large and therefore pruning becomes an essential task. In this paper, we survey different rule pruning methods used by current associative classification techniques. Further, we compare the effect of three pruning methods (database coverage, pessimistic error estimation, lazy pruning) on the accuracy rate and the number of rules derived from different classification data sets. Results obtained from experimenting on different data sets from UCI data collection indicate that lazy pruning algorithms may produce slightly higher predictive classifiers than those which utilise database coverage and pessimistic error pruning methods. However, the potential use of such classifiers is limited because they are difficult to understand and maintain by the end-user.},
	keywords = {Association rule, Associative classification, Classification, Data mining, Rule pruning},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749400880\&partnerID=40\&md5=0633368b3744eefc60e6e205c64d95f4},
	author = {Thabtah, F.}
}
