Fundamentals of database systems
Fundamentals of database systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Declarative Bias for Specific-to-General ILP Systems
Machine Learning - Special issue on bias evaluation and selection
Fast discovery of association rules
Advances in knowledge discovery and data mining
Top-down induction of first-order logical decision trees
Artificial Intelligence
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Declarative Language Bias for Levelwise Search of First-Order Regularities
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Relational Knowledge Discovery in Databases
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Discovery of First-Order Regularities in a Relational Database Using Offline Candidate Determination
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Multi-relational Data Mining: a perspective
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Relational Association Rules: Getting WARMeR
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
ONTOLOGER: a system for usage-driven management of ontology-based information portals
Proceedings of the 2nd international conference on Knowledge capture
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
ACM Transactions on Database Systems (TODS)
Short communication: A new relational learning system using novel rule selection strategies
Knowledge-Based Systems
Extracting constraints for process modeling
Proceedings of the 4th international conference on Knowledge capture
k-RNN: k-relational nearest neighbour algorithm
Proceedings of the 2008 ACM symposium on Applied computing
Efficient and Scalable Induction of Logic Programs Using a Deductive Database System
Inductive Logic Programming
A Mining Algorithm Using Property Items Extracted from Sampled Examples
Inductive Logic Programming
Description and classification of complex structured objects by applying similarity measures
International Journal of Approximate Reasoning
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Combining Multiple Interrelated Streams for Incremental Clustering
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
On mining closed sets in multi-relational data
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Implementing Multi-relational Mining with Relational Database Systems
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Relational pattern mining based on equivalent classes of properties extracted from samples
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A comparative study on ILP-based concept discovery systems
Expert Systems with Applications: An International Journal
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Relational association mining based on structural analysis of saturation clauses
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Discovering interesting information with advances in web technology
ACM SIGKDD Explorations Newsletter
AMIE: association rule mining under incomplete evidence in ontological knowledge bases
Proceedings of the 22nd international conference on World Wide Web
Mining closed patterns in relational, graph and network data
Annals of Mathematics and Artificial Intelligence
| Hi-index | 0.00 |
Within KDD, the discovery of frequent patterns has been studied in a variety of settings. In its simplest form, known from association rule mining, the task is to discover all frequent item sets, i.e., all combinations of items that are found in a sufficient number of examples. We present algorithms for relational association rule discovery that are well-suited for exploratory data mining. They offer the flexibility required to experiment with examples more complex than feature vectors and patterns more complex than item sets.