ECML '95 Proceedings of the 8th European Conference on Machine Learning
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Maximally informative k-itemsets and their efficient discovery
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
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
Mining correlated subgraphs in graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A framework for pattern-based global models
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
Constraint programming for mining n-ary patterns
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Fast extraction of locally optimal patterns based on consistent pattern function variations
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Fast, effective molecular feature mining by local optimization
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Evaluating pattern set mining strategies in a constraint programming framework
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
A generic approach for modeling and mining n-ary patterns
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Non-redundant subgroup discovery in large and complex data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Combining CSP and constraint-based mining for pattern discovery
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part II
A constraint language for declarative pattern discovery
Proceedings of the 27th Annual ACM Symposium on Applied Computing
An enhanced relevance criterion for more concise supervised pattern discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical significance testing approach to mining the most informative set of patterns
Data Mining and Knowledge Discovery
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Pattern discovery algorithms typically produce many interesting patterns. In most cases, patterns are reported based on their individual merits, and little attention is given to the interestingness of a pattern in the context of other patterns reported. In this paper, we propose filtering the returned set of patterns based on a number of quality measures for pattern sets. We refer to a small subset of patterns that optimises such a measure as a pattern team. A number of quality measures, both supervised and unsupervised, is proposed. We analyse to what extent each of the measures captures a number of ‘intuitions' users may have concerning effective and informative pattern teams. Such intuitions involve qualities such as independence of patterns, low overlap, and combined predictiveness.