Relational Association Rules: Getting WARMeR

In recent years, the problem of association rule mining in transactional data has been well studied. We propose to extend the discovery of classical association rules to the discovery of association rules of conjunctive queries in arbitrary relational data, inspired by the WARMR algorithm, developed by Dehaspe and Toivonen, that discovers association rules over a limited set of conjunctive queries. Conjunctive query evaluation in relational databases is well understood, but still poses some great challenges when approached from a discovery viewpoint in which patterns are generated and evaluated with respect to some well defined search space and pruning operators.

[1]  Shan-Hwei Nienhuys-Cheng,et al.  Foundations of Inductive Logic Programming , 1997, Lecture Notes in Computer Science.

[2]  Luc Dehaspe,et al.  Discovery of relational association rules , 2001 .

[3]  Jeffrey D. Uuman.  Principles of database and knowledge- base systems , 1989 .

[4]  Toon Calders,et al.  Mining All Non-derivable Frequent Itemsets , 2002, PKDD.

[5]  Jennifer Widom,et al.  Database System Implementation , 2000 .

[6]  Anand Rajaraman,et al.  Conjunctive query containment revisited , 1997, Theor. Comput. Sci..

[7]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

[8]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[9]  Hannu Toivonen,et al.  Discovery of frequent DATALOG patterns , 1999, Data Mining and Knowledge Discovery.

[10]  Prasan Roy,et al.  Efficient and extensible algorithms for multi query optimization , 1999, SIGMOD '00.

[11]  Mohammed J. Zaki.  Efficiently mining frequent trees in a forest , 2002, KDD.

[12]  George Karypis,et al.  Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[13]  Jeffrey D. Ullman,et al.  Principles of Database and Knowledge-Base Systems, Volume II , 1988, Principles of computer science series.

[14]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[15]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[16]  Jean-François Boulicaut,et al.  Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries , 2004, Data Mining and Knowledge Discovery.