Privacy-Preserving Frequent Pattern Mining across Private Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning quantifiable associations via principal sparse non-negative matrix factorization
Intelligent Data Analysis
FAT-miner: mining frequent attribute trees
Proceedings of the 2007 ACM symposium on Applied computing
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
Mining interesting sets and rules in relational databases
Proceedings of the 2010 ACM Symposium on Applied Computing
Pattern mining on stars with FP-growth
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Expert Systems with Applications: An International Journal
Mining quantitative associations in large database
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Extending the UML for designing association rule mining models for data warehouses
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Finding patterns in large star schemas at the right aggregation level
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
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Association rule mining is an important data mining problem.It is found to be useful for conventional relational data.However, previous work had mostly targeted on mining a single table.In real life, a database is typically made up of multiple table and one important case is where some of the tables form a star schema.That tables typically correspond to entity sets and joining the tables in a star schema gives relationship amoung entity sets which can be very interesting information.Hence mining on the join result is an important problem.Based on characteristics of the star schema we propose an efficient algorithm for mining association rules on the joinresult but without actually performing the join opertation.We show that this approach can significantly out-perform the join-then-mine approach even when the latter adopts a fastest known mining algorithm.