Discovery of frequent DATALOG patterns
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
A Mining Algorithm Using Property Items Extracted from Sampled Examples
Inductive Logic Programming
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
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We propose an algorithm for multi-relational pattern mining through the problem established in WARMR. In order to overcome the combinatorial problem of large pattern space, another algorithm MAPIX restricts patterns into combination of basic patterns, called properties. A property is defined as a set of literals appeared in examples and is of an extended attribute-value form. Advantage of MAPIX is to make patterns from pattern fragments occurred in examples. Many patterns which are not appeared in examples are not tested. Although the range of patterns is clear and MAPIX enumerates them efficiently, a large part of patterns are out of the range. The proposing algorithm keeps the advantage and extends the way of combination of properties. The algorithm combines properties as they appeared in examples, we call it structure preserving combination.