Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Cluster_KDD: a visual clustering and knowledge discovery platform based on concept lattice
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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In this paper, we are interested in the knowledge discovery methods. The major inconveniences of these methods are: i) the generation of a big number of association rules that are not easily assimilated by the human brain ii) the space memory and the time execution necessary for the management of their data structures. To cure this problem, we propose to build rules (meta-rules) between groups (or clusters) resulting from a preliminary fuzzy clustering on the data. We prove that we can easily deduce knowledge about the initial data set if we want more details. This solution reduced considerably the number of generated rules, offered a better interpretation of the data and optimized both the space memory and the execution time. This approach is extensible; the user is able to choose the fuzzy clustering or the extraction rules algorithm according to the domain of his data and his needs.