Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Empirical bayes screening for multi-item associations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
DMajor—Application Programming Interface for Database Mining
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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A framework to support multiple query optimization for complex mining tasks
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Soft constraint based pattern mining
Data & Knowledge Engineering
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
An efficient data mining approach for discovering interesting knowledge from customer transactions
Expert Systems with Applications: An International Journal
Summary queries for frequent itemsets mining
Journal of Systems and Software
Extending the soft constraint based mining paradigm
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
UFRGS@CLEF2008: using association rules for cross-language information retrieval
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Constraint relaxations for discovering unknown sequential patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Ontology-based filtering mechanisms for web usage patterns retrieval
EC-Web'05 Proceedings of the 6th international conference on E-Commerce and Web Technologies
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Ontology-Based rummaging mechanisms for the interpretation of web usage patterns
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Interactive association rules discovery
ICFCA'06 Proceedings of the 4th international conference on Formal Concept Analysis
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The common approach to exploit mining constraints is to push them deeply into the mining algorithms. In our paper we argue that this approach is based on an understanding of KDD that is no longer up-to-date. In fact, today KDD is seen as a human centered, highly interactive and iterative process. Blindly enforcing constraints already during the mining runs neglects the process character of KDD and therefore is no longer state of the art. Constraints can make a single algorithm run faster but in fact we are still far from response times that would allow true interactivity in KDD. In addition we pay the price of repeated mining runs and moreover risk reducing data mining to some kind of hypothesis testing. Taking all the above into consideration we propose to do exactly the contrary of constrained mining: We accept an initial (nearly) unconstrained and costly mining run. But instead of a sequence of subsequent and still expensive constrained mining runs we answer all further mining queries from this initial result set. Whereas this is straight forward for constraints that can be implemented as filters on the result set, things get more complicated when we restrict the underlying mining data. Actually in practice such constraints are very important, e.g. the generation of rules for certain days of the week, for families, singles, male or female customers etc. We show how to postpone such row-restriction constraints on the transactions from rule generation to rule retrieval from the initial result set.