A database perspective on knowledge discovery
Communications of the ACM
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Condensed representation of EPs and patterns quantified by frequency-based measures
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Proceedings of the 2004 international conference on Local Pattern Detection
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Extraction of association rules based on literalsets
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Adequate Condensed Representations of Patterns
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Data & Knowledge Engineering
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
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
DS'10 Proceedings of the 13th international conference on Discovery science
New exact concise representation of rare correlated patterns: application to intrusion detection
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Mining high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
Information Sciences: an International Journal
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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Patterns are at the core of the discovery of a lot of knowledge from data but their uses are limited due to their huge number and their mining cost. During the last decade, many works addressed the concept of condensed representation w.r.t. frequency queries. Such representations are several orders of magnitude smaller than the size of the whole collections of patterns, and also enable us to regenerate the frequency information of any pattern. In this paper, we propose a framework for condensed representations w.r.t. a large set of new and various queries named condensable functions based on interestingness measures (e.g., frequency, lift, minimum). Such condensed representations are achieved thanks to new closure operators automatically derived from each condensable function to get adequate condensed representations. We propose a generic algorithm Mic Mac to efficiently mine the adequate condensed representations. Experiments show both the conciseness of the adequate condensed representations and the efficiency of our algorithm.