C4.5: programs for machine learning
C4.5: programs for machine learning
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
Efficient mining of association rules using closed itemset lattices
Information Systems
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth 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
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On Local Pruning of Association Rules Using Directed Hypergraphs
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Data Mining and Knowledge Discovery
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
Association rules mining using heavy itemsets
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
Generating concise association rules
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Effective elimination of redundant association rules
Data Mining and Knowledge Discovery
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
An improved association rules mining method
Expert Systems with Applications: An International Journal
International Journal of Applied Metaheuristic Computing
BruteSuppression: a size reduction method for Apriori rule sets
Journal of Intelligent Information Systems
Rule acquisition and complexity reduction in formal decision contexts
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
Besides preprocessing, post-analysis also plays an important role in knowledge discovery. It can effectively assist users to grasp the obtained knowledge. However, many of data mining algorithms merely take performance into consideration and put the post-analysis of results aside. They generate a modest number of rules for the purpose of improving accuracy. Unfortunately, most induced rules are redundant or insignificant. Their presence not only confuses end-users in post-analysis, but also degrades efficiency in future decision task. Thus, it is necessary to eliminate redundant or irrelevant rules as more as possible. In this paper, we present an efficient post-processing method to prune redundant rules by virtue of the property of Galois connection, which inherently constrains rules with respect to objects. Its advantage is that information will not be lost greatly during pruning step. The experimental evaluation shows that the proposed method is competent for discarding a large number of superfluous rules effectively and a high compression factor will be achieved. What's more, the computational cost of our method is surprisingly lower than the Apriori method.