Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Introduction to logical information systems
Information Processing and Management: an International Journal
On stability of a formal concept
Annals of Mathematics and Artificial Intelligence
Condensed Representation of Sequential Patterns According to Frequency-Based Measures
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Analysis of social communities with iceberg and stability-based concept lattices
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Formal concept analysis enhances fault localization in software
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Knowledge-Based Interactive Postmining of Association Rules Using Ontologies
IEEE Transactions on Knowledge and Data Engineering
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Data mining techniques are used in order to discover emerging knowledge (patterns) in databases. The problem of such techniques is that there are, in general, too many resulting patterns for a user to explore them all by hand. Some methods try to reduce the number of patterns without a priori pruning. The number of patterns remains, nevertheless, high. Other approaches, based on a total ranking, propose to show to the user the top-k patterns with respect to a measure. Those methods do not take into account the user's knowledge and the dependencies that exist between patterns. In this paper, we propose a new way for the user to explore extracted patterns. The method is based on navigation in a partial order over the set of all patterns in the Logical Concept Analysis framework. It accommodates several kinds of patterns and the dependencies between patterns are taken into account thanks to partial orders. It allows the user to use his/her background knowledge to navigate through the partial order, without a priori pruning. We illustrate how our method can be applied on two different tasks (software engineering and natural language processing) and two different kinds of patterns (association rules and sequential patterns).