C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient mining of association rules using closed itemset lattices
Information Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Prince: an algorithm for generating rule bases without closure computations
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On pruning and tuning rules for associative classifiers
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A Parameter-Free Associative Classification Method
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
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Many studies in data mining have proposed a new classification approach called associative classification. According to several reports associative classification achieves higher classification accuracy than do traditional classification approaches. However, the associative classification suffers from a major drawback: it is based on the use of a very large number of classification rules; and consequently takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose a new associative classification method called Garc that exploits a generic basis of association rules in order to reduce the number of association rules without jeopardizing the classification accuracy. Moreover, Garc proposes a new selection criterion called score, allowing to ameliorate the selection of the best rules during classification. Carried out experiments on 12 benchmark data sets indicate that Garc is highly competitive in terms of accuracy in comparison with popular associative classification methods.