Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Decomposable Families of Itemsets
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Pleiades: Subspace Clustering and Evaluation
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
A framework for pattern-based global models
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A framework for mining interesting pattern sets
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Pattern selection problems in multivariate time-series using equation discovery
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Constraint programming for mining n-ary patterns
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Summarising data by clustering items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
A framework for mining interesting pattern sets
ACM SIGKDD Explorations Newsletter
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Fast and memory-efficient discovery of the top-k relevant subgroups in a reduced candidate space
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
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
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Mining contextual preference rules for building user profiles
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Discovering descriptive tile trees: by mining optimal geometric subtiles
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
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Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than a human expert or a machine learning technique could make use of. Often different patterns or combinations of patterns cover a similar subset of the examples, thus being redundant and not carrying any new information. To remove the redundant information contained in such pattern sets, we propose a general heuristic approach for selecting a small subset of patterns. We identify several selection techniques for use in this general algorithm and evaluate those on several data sets. The results show that the technique succeeds in severely reducing the number of patterns, while at the same time apparently retaining much of the original information. Additionally the experiments show that reducing the pattern set indeed improves the quality of classification results. Both results show that the approach is very well suited for the goals we aim at.