An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Discovery of relational association rules
Relational Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Induction of Logic Programs with More Than One Recursive Clause by Analyzing Saturations
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
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
Multi-relational pattern mining system for general database systems
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
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This paper extends the bottom-up relational miner Mapix[9]. It takes a relational database consists of multiple relational tables including a target relation, and enumerates patterns with which a large part of instances in the target relation match. The patterns are given as logical formulae. Although a well-known system Warmr generates and tests possible patterns, it has limitation in its efficiency. Mapix took a bottom-up approach and gained efficiency at the cost of variety of patterns. It searches and propositionalizes features appeared in instances. Patterns produced is only simple combinations of attributed. The proposed algorithm EquivPix (an equivalent-class-based miner using property items extracted from examples) keeps the merits of bottom-up approach, i.e. time-efficiency and prohibition of duplicated patterns, and it widens pattern variation. EquivPix introduces equivalent classes on properties extracted and also two combination operators of them.