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Erschienen in:
2005 | OriginalPaper | Buchkapitel
SQL Based Frequent Pattern Mining with FP-Growth
verfasst von : Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist
Erschienen in: Applications of Declarative Programming and Knowledge Management
Verlag: Springer Berlin Heidelberg
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Scalable data mining in large databases is one of today’s real challenges to database research area. The integration of data mining with database systems is an essential component for any successful large-scale data mining application. A fundamental component in data mining tasks is finding frequent patterns in a given dataset. Most of the previous studies adopt an
Apriori
-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this study we present an evaluation of SQL based frequent pattern mining with a novel frequent pattern growth (
FP
-
growth
) method, which is efficient and scalable for mining both long and short patterns without candidate generation. We examine some techniques to improve performance. In addition, we have made performance evaluation on DBMS with IBM DB2 UDB EEE V8.