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
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth 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
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Inferring Knowledge from Frequent Patterns
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Theory of Inductive Query Answering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Database transposition for constrained (closed) pattern mining
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Optimizing Constraint-Based Mining by Automatically Relaxing Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A model for managing collections of patterns
Proceedings of the 2007 ACM symposium on Applied computing
Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Efficient mining under rich constraints derived from various datasets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Itemset mining: A constraint programming perspective
Artificial Intelligence
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
Exploiting virtual patterns for automatically pruning the search space
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
A new class of constraints for constrained frequent pattern mining
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A constrained frequent pattern mining system for handling aggregate constraints
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Para Miner: a generic pattern mining algorithm for multi-core architectures
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Constraint-based mining is an active field of research which is a key point to get interactive and successful KDD processes. Nevertheless, usual solvers are limited to particular kinds of constraints because they rely on properties to prune the search space which are incompatible together. In this paper, we provide a general framework dedicated to a large set of constraints described by SQL-like and syntactic primitives. This set of constraints covers the usual classes and introduces new tough and flexible constraints. We define a pruning operator which prunes the search space by automatically taking into account the characteristics of the constraint at hand. Finally, we propose an algorithm which efficiently makes use of this framework. Experimental results highlight that usual and new complex constraints can be mined in large datasets.