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2004

Efficient Frequent Pattern Mining in Relational Databases

9 years 14 days ago
Efficient Frequent Pattern Mining in Relational Databases
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 (PROjection PAttern Discovery). Propad fundamentally differs from an Apriorilike 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 [Sarawagi e...
Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where LWA
Authors Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist
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