Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Rules as Attributes in Classifier Construction
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining Associations by Linear Inequalities
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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Constructing associative classifier is to use the technique of mining association rules to extract attribute-value pairs that are associated with class labels. Since too many such kinds of associations may be generated however, the existing algorithms to finding associations are usually ineffective. It is well known that rough sets theory can be used to select reducts of attributes that represent the original data set. In this paper we present an approach of combining the rough set theory, the association rules mining technique, and the covering method to construct classification rules. With a given decision table, the rough set theory is first used to find all reducts of condition attributes of the decision table. Then an adapted Apriori algorithm to mining association rules is used to find a set of associative classifications from each reduct. And third, all association classification rules are ranked according to their importance, support, and confidence and selected in sequence to build a classifier with high accuracy. An example illustrates how this approach works.