Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
The relational model for database management: version 2
The relational model for database management: version 2
On the foundations of the universal relation model
ACM Transactions on Database Systems (TODS)
A simplied universal relation assumption and its properties
ACM Transactions on Database Systems (TODS)
Maximal objects and the semantics of universal relation databases
ACM Transactions on Database Systems (TODS)
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Database Systems Concepts
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Indexing and Mining Free Trees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Efficient Data Mining for Maximal Frequent Subtrees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
ART: A Hybrid Classification Model
Machine Learning
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Cross-relational clustering with user's guidance
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
The Definitive Guide to db4o
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
FAT-miner: mining frequent attribute trees
Proceedings of the 2007 ACM symposium on Applied computing
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Database Systems: The Complete Book
Database Systems: The Complete Book
Spatiotemporal Relational Probability Trees: An Introduction
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Top-down induction of first-order logical decision trees
Artificial Intelligence
POTMiner: mining ordered, unordered, and partially-ordered trees
Knowledge and Information Systems
Frequent tree pattern mining: A survey
Intelligent Data Analysis
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
Mining patterns from longitudinal studies
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Genetic algorithm-based optimized association rule mining for multi-relational data
Intelligent Data Analysis
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This paper proposes a new approach to mine multirelational databases. Our approach is based on the representation of multirelational databases as sets of trees, for which we propose two alternative representation schemes. Tree mining techniques can thus be applied as the basis for multirelational data mining techniques, such as multirelational classification or multirelational clustering. We analyze the differences between identifying induced and embedded tree patterns in the proposed tree-based representation schemes and we study the relationships among the sets of tree patterns that can be discovered in each case. This paper also describes how these frequent tree patterns can be used, for instance, to mine association rules in multirelational databases.