Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
HDB-Subdue: A Scalable Approach to Graph Mining
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Enhanced DB-Subdue: supporting subtle aspects of graph mining using a relational approach
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A framework for SQL-Based mining of large graphs on relational databases
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Data mining aims at discovering important and previously unknown patterns from the dataset in the underlying database. Database mining performs mining directly on data stored in (relational) database management systems (RDBMSs). The type of underlying database can vary and should not be a constraint on the mining process. Irrespective of the database in which data is stored, we should be able to mine the data. Several SQL92 approaches (such as K-way join, Query/Subquery, and Two-group by) have been studied in the literature. In this paper, we focus on the K-way join approach. We study several additional optimizations for the K-way join approach and evaluate them using DB2 and Oracle RDBMSs. We evaluate the approaches analytically and compare their performance on large data sets. Finally, we summarize the results and indicate the conditions for which the individual optimizations are useful. The larger goal of this work is to feed these results into a layered optimizer that chooses specific strategies based on the input dataset characteristics.