Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Discovering Structural Association of Semistructured Data
IEEE Transactions on Knowledge and Data Engineering
Optimized Substructure Discovery for Semi-structured Data
PKDD '02 Proceedings of the 6th European Conference on Principles of 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
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Association Rules from Stars
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ACM SIGIR Forum
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Characteristic relational patterns
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of frequent graph patterns that consist of the vertices with the complex structures
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Using trees to mine multirelational databases
Data Mining and Knowledge Discovery
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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Data that can conceptually be viewed as tree structures abounds in domains such as bio-informatics, web logs, XML databases and multi-relational databases. Besides structural information such as nodes and edges, tree structured data also often contains attributes, that represent properties of nodes. Current algorithms for finding frequent patterns in structured data, do not take these attributes into account, and hence potentially useful information is neglected. We present FAT-miner, an algorithm for frequent pattern discovery in tree structured data with attributes. To illustrate the applicability of FAT-miner, we use it to explore the properties of good and bad loans in a well-known multi-relational financial database.