An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
SQL database primitives for decision tree classifiers
Proceedings of the tenth international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Association Analysis with One Scan of Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Processing frequent itemset discovery queries by division and set containment join operators
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
SQL based frequent pattern mining without candidate generation
Proceedings of the 2004 ACM symposium on Applied computing
Processing sequential patterns in relational databases
Journal on data semantics VIII
Processing sequential patterns in relational databases
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Frequent itemset mining with parallel RDBMS
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation since the prohibitive nature of the cost associated with extracting knowledge, as well as the lack of suitable declarative query language support. We investigate approaches based on SQL for the problem of finding frequent patterns from a transaction table, including an algorithm that we recently proposed, called Propad (PRO-jection PAttern Discovery). Propad fundamentally differs from an Apriori-like candidate set generation-and-test approach. This approach successively projects the transaction table into frequent itemsets to avoid making multiple passes over the large original transaction table and generating a huge sets of candidates. We have made performance evaluation on DBMS (IBM DB2 UDB EEE V8) and compared the performance results with K-Way join approach proposed in [11] and SQL based FP-tree approach proposed in [13]. The experimental results show that our algorithm can get efficient performance.