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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

期数:

2007年08期

页码:

84-87

栏目:

智能、算法、系统工程

出版日期:

1900-01-01

文章信息/Info

Title:

An Improved Algorithm for Mining Sequential Pattern Based on Bitmap

文章编号:

1673-629X（2007）08-0084-04

作者:

王红侠 胡学钢

合肥工业大学计算机与信息学院

Author(s):

WANG Hong-xia;  HU Xue-gang

Computer ＆ Information Technology Institute, Hefei University of Technology

关键词:

KDD; 位图; 序列模式

Keywords:

KDD;  bitmap;  sequential patterns

分类号:

TP18 TP301.6

文献标志码:

A

摘要:

结合BBSP,提出了一种称做最终位置归纳序列模式挖掘（LPI-SPM）的新算法,该算法可以有效地从大型数据库中获取所有的频繁序列模式。该策略与以前工作的不同点在于：当判断一个序列是否是模式时,通过扫描数据库创建S-矩阵来实现（PrefixSpan）或者通过对候选项进行交运算（SPADE）或并运算（BBSP）统计其数量来实现。相反,在基于下列事实的基础上LPI-SPN会很容易实施这一过程,即若一个项的最终位置小于当前前缀位置,在相同的顾客序列中,该项就不会出现在当前前缀的后面。LPI-SPM在序列挖掘过程中

Abstract:

In this paper, by combining BBSP（bitmap based sequential patterns）, propose a new algorithm called Last Position Induction Sequential Pattern Mining （LPI - SPM）, Which can efficiently get all the frequent sequential patterns from a large database. The main difference between our strategy and the previous works is that when judging whether a sequence is a pattern or not, they use S- Matrix by scanning projected database （PrefixSpan） or count the number by joining （SPADE） or ANDing with the candidate item （BBSP）. In contrast, LPI - SPM can easily implement this process based on the following fact - if an item＇s last position is smaller than the current prefix position, the item can not appear behind the current prefix in the same customer sequence. LPI- SPM could largely reduce the search space during raining process and is considerable effectiveness in mining sequential pattern. Our experimental results show that LPI - SPM outperforms BBSP up to three times on all kinds of dataset

备注/Memo

备注/Memo:

王红侠（1969-），女，安徽宿州人，讲师，硕士研究生，研究方向为KDD；胡学钢，教授，硕士生导师，研究方向为KDD、人工智能、算法设计等

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更新日期/Last Update: 1900-01-01