Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Database systems—breaking out of the box
ACM SIGMOD Record
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM SIGKDD Explorations Newsletter
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Data Mining of Association Rules and the Process of Knowledge Discovery in Databases
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CrystalBall: a framework for mining variants of association rules
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
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
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Scrutinizing Frequent Pattern Discovery Performance
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining fuzzy temporal patterns from process instances with weighted temporal graphs
International Journal of Data Analysis Techniques and Strategies
An efficient data mining approach for discovering interesting knowledge from customer transactions
Expert Systems with Applications: An International Journal
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
PARAS: a parameter space framework for online association mining
Proceedings of the VLDB Endowment
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Although there have been many studies on data mining, to date there have been few research prototypes or commercial systems supporting comprehensive query-driven mining, which encourages interactive exploration of the data. Our thesis is that constraint constructs and the optimization they induce play a pivotal role in mining queries, thus substantially enhancing the usefulness and performance of the mining system. This is based on the analogy of declarative query languages like SQL and query optimization which have made relational databases so successful. To this end, our proposed demo is not yet another data mining system, but of a new paradigm in data mining - mining with constraints, as the important first step towards supporting ad-hoc mining in DBMS.In this demo, we will show a prototype exploratory mining system that implements constraint-based mining query optimization methods proposed in [5]. We will demonstrate how a user can interact with the system for exploratory data mining and how efficiently the system may execute optimized data mining queries. The prototype system will include all the constraint pushing techniques for mining association rules outlined in [5], and will include additional capabilities for mining other kinds of rules for which the computation of constrained frequent sets forms the core first step.