Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Designing Templates for Mining Association Rules
Journal of Intelligent Information Systems
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough set algorithms in classification problem
Rough set methods and applications
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Postprocessing in machine learning and data mining
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
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
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Data Mining and Knowledge Discovery
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Editorial: Guest editors' introduction
Data & Knowledge Engineering
Data Mining and Knowledge Discovery
Using metarules to organize and group discovered association rules
Data Mining and Knowledge Discovery
Reducing redundancy in characteristic rule discovery by using integer programming techniques
Intelligent Data Analysis
Redundant association rules reduction techniques
International Journal of Business Intelligence 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 survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Medical datasets analysis: a constructive induction approach
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A new hybrid method of generation of decision rules using the constructive induction mechanism
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Simple and effective behavior tracking by post processing of association rules into segments
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
International Journal of Applied Metaheuristic Computing
| Hi-index | 12.05 |
For a classifier, besides classification capability, its size is another vital aspect. In pursuit of high performance, many classifiers do not take into consideration their sizes and contain numerous both essential and insignificant rules. This, however, may bring adverse situation to classifier, for its efficiency will been put down greatly by redundant rules. Hence, it is necessary to eliminate those unwanted rules. In this paper, we propose a fast post-processing approach to remove insignificant rules. The basis of this method is the dependent relation between rules regarding to data objects, from which closed sets can be derived. The experimental evaluation on UCI benchmark datasets using two typical classifiers shows that the proposed method is competent for discarding lots of superfluous rules without degrading classification capability greatly. In particular, the computational cost of our approach is extremely lower than the Apriori-like method.