SQL based frequent pattern mining with FP-growth

Xuequn Shang; Kai Uwe Sattler; Ingolf Geist

doi:10.1007/11415763_3

SQL based frequent pattern mining with FP-growth

Xuequn Shang
, Kai Uwe Sattler
, Ingolf Geist

Otto von Guericke University Magdeburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Applic. of Declarative Program. and Knowledge Manage. - 15th Int. Conf. on Applications of Declarative Program. and Knowledge Manage., INAP 2004, and 18th Workshop on Logic Program., WLP 2004
Publisher	Springer Verlag
Pages	32-46
Number of pages	15
ISBN (Print)	3540255605, 9783540255604
DOIs	https://doi.org/10.1007/11415763_3
State	Published - 2005
Externally published	Yes
Event	15th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2004, and 18th Workshop on Logic Programming, WLP 2004 - Applications of Declarative Programming and Knowledge Management - Potsdam, Germany Duration: 4 Mar 2004 → 6 Mar 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3392 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2004, and 18th Workshop on Logic Programming, WLP 2004 - Applications of Declarative Programming and Knowledge Management
Country/Territory	Germany
City	Potsdam
Period	4/03/04 → 6/03/04

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10.1007/11415763_3

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Shang, X., Sattler, K. U., & Geist, I. (2005). SQL based frequent pattern mining with FP-growth. In Applic. of Declarative Program. and Knowledge Manage. - 15th Int. Conf. on Applications of Declarative Program. and Knowledge Manage., INAP 2004, and 18th Workshop on Logic Program., WLP 2004 (pp. 32-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3392 LNAI). Springer Verlag. https://doi.org/10.1007/11415763_3

Shang, Xuequn ; Sattler, Kai Uwe ; Geist, Ingolf. / SQL based frequent pattern mining with FP-growth. Applic. of Declarative Program. and Knowledge Manage. - 15th Int. Conf. on Applications of Declarative Program. and Knowledge Manage., INAP 2004, and 18th Workshop on Logic Program., WLP 2004. Springer Verlag, 2005. pp. 32-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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Shang, X, Sattler, KU & Geist, I 2005, SQL based frequent pattern mining with FP-growth. in Applic. of Declarative Program. and Knowledge Manage. - 15th Int. Conf. on Applications of Declarative Program. and Knowledge Manage., INAP 2004, and 18th Workshop on Logic Program., WLP 2004. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3392 LNAI, Springer Verlag, pp. 32-46, 15th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2004, and 18th Workshop on Logic Programming, WLP 2004 - Applications of Declarative Programming and Knowledge Management, Potsdam, Germany, 4/03/04. https://doi.org/10.1007/11415763_3

SQL based frequent pattern mining with FP-growth. / Shang, Xuequn; Sattler, Kai Uwe; Geist, Ingolf.
Applic. of Declarative Program. and Knowledge Manage. - 15th Int. Conf. on Applications of Declarative Program. and Knowledge Manage., INAP 2004, and 18th Workshop on Logic Program., WLP 2004. Springer Verlag, 2005. p. 32-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3392 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Shang X, Sattler KU, Geist I. SQL based frequent pattern mining with FP-growth. In Applic. of Declarative Program. and Knowledge Manage. - 15th Int. Conf. on Applications of Declarative Program. and Knowledge Manage., INAP 2004, and 18th Workshop on Logic Program., WLP 2004. Springer Verlag. 2005. p. 32-46. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11415763_3

SQL based frequent pattern mining with FP-growth

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