SPADE: An efficient algorithm for mining frequent sequences

被引：1703

作者：

Zaki, MJ ^{[1
]}

机构：

[1] Rensselaer Polytech Inst, Dept Comp Sci, Troy, NY 12180 USA

来源：

MACHINE LEARNING | 2001年 / 42卷 / 1-2期

关键词：

sequence mining; sequential patterns; frequent patterns; data mining; knowledge discovery;

D O I：

10.1023/A:1007652502315

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations. All sequences are discovered in only three database scans. Experiments show that SPADE outperforms the best previous algorithm by a factor of two, and by an order of magnitude with some pre-processed data. It also has linear scalability with respect to the number of input-sequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.

引用

页码：31 / 60

页数：30