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
Fast discovery of association rules
Advances in knowledge discovery and data mining
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 association rules using closed itemset lattices
Information Systems
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Approximation of Frequency Queris by Means of Free-Sets
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Modeling KDD Processes within the Inductive Database Framework
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
A Comparison between Query Languages for the Extraction of Association Rules
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Approximate Query Answering with Frequent Sets and Maximum Entropy
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Optimization of association rule mining queries
Intelligent Data Analysis
Optimization of a language for data mining
Proceedings of the 2003 ACM symposium on Applied computing
Reducing borders of k-disjunction free representations of frequent patterns
Proceedings of the 2004 ACM symposium on Applied computing
Mining databases and data streams with query languages and rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. Querying these databases needs for primitives to: (1) select, manipulate and query data, (2) select, manipulate and query "interesting" patterns (i.e., those patterns that satisfy certain constraints), and (3) cross over patterns and data (e.g., selecting the data in which some patterns hold). Designing such query languages is a long-term goal and only preliminary approaches have been studied, mainly for the association rule mining task. Starting from a discussion on the MINE RULE operator, we identify several open issues for the design of inductive databases dedicated to these descriptive rules. These issues concern not only the offered primitives but also the availability of efficient evaluation schemes. We emphasize the need for primitives that work on more or less condensed representations for the frequent itemsets, e.g., the (frequent) 驴-free and closed itemsets. It is useful not only for optimizing single association rule mining queries but also for sophisticated post-processing and interactive rule mining.