@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 {323,
	title = {CMCCR: Classification based on multiple class-correlation rules},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {64 - 70},
	abstract = {Previous studies show that associative classification achieves higher classification accuracy than traditional classification approaches. However, associative classification suffers from two major deficiencies and thus sometimes has lower classification accuracy. First, it often generates a large number of rules when the minimum support is set to be low. It is not only time consuming to discover so many itemsets, but also very difficult to select high quality rules from a huge set of rules. Second, the association measure confidence does not reflect correlation relationships between itemsets and the class label. To deal these problems, in this paper, we propose a new classification approach called classification based on multiple class-correlation rules (CMCCR). First, we use both support and all-confidence to mine not only frequent but also mutually associated itemsets. This method not only saves the running time but as well considerably reduces the number of itemsets generated, and thus sharply decreases the number of class-correlation rules produced. Second, we use a new correlation measure correlation confidence to discover class-correlation rules. The measure correlation confidence has two bounds:- 1and 1. It is easy to control the correlation degree of the class-correlation rules found. Finally, we use multiple class-correlation rules and average correlation degree to measure the combined effect of group rules. Experimental results on the mushroom data set show that CMCCR has higher and more stable accuracy than associative classification and decision tree method.},
	keywords = {All-confidence, Class-correlation rule, Classification, Correlation confidence},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866448185\&partnerID=40\&md5=10fb4c8ff12d02f2555fb4ba4c8f5d04},
	author = {Zhou, Z. and Wang, X. and Pan, G.}
}
