Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent Itemset Counting Across Multiple Tables
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining Association Rules from Stars
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Prospects and challenges for multi-relational data mining
ACM SIGKDD Explorations Newsletter
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
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Most existing data mining (DM) approaches look for patterns in a single table. Multi-relational DM approaches, on the other hand, look for patterns that involve multiple tables. In recent years, the most common DM techniques have been extended to the multirelational case, but there are few dedicated to star schemas. These schemas are composed of a central fact table, linking a set of dimension tables, and joining all the tables before mining may not be a feasible solution. This work proposes a method for frequent pattern mining in a star schema based on FP-Growth. It does not materialize the entire join between the tables. Instead, it constructs an FP-Tree for each dimension and then combines them to form a super FP-Tree, that will serve as input to FP-Growth.