@inproceedings{9111760f25b945939acbd72b92939dc3,
title = "SQL based frequent pattern mining with FP-growth",
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.",
author = "Xuequn Shang and Sattler, {Kai Uwe} and Ingolf Geist",
year = "2005",
doi = "10.1007/11415763_3",
language = "英语",
isbn = "3540255605",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "32--46",
booktitle = "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",
note = "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 ; Conference date: 04-03-2004 Through 06-03-2004",
}